Amira Code And Guidelines Release 3 20070219 4f3312

P754: Metal ing Code of Practice and Guidelines: Release 3

February 2007

© AMIRA International

AMIRA International Africa Registration No. 2002/011077/08 88 Studio Office Park, 5 Concourse Crescent, Lonehill, South Africa. Phone: +27 11 745 1522, Facsimile: +27 21 467 0021 Email: [email protected] Website: www.amira.com.au

To the Sponsors of the AMIRA P754 Project The third formal release of the Code of Practice for Metal ing, dated February 2007, is attached for your attention. Also attached is the third release of the Guidelines Document, which s the Code of Practice. These documents have been prepared in consultation with representatives of the sponsor companies, particularly those who serve on the Project Steering Committee. Following the first release of these documents in October 2005, extensive and comments were received from Dr GJ Lyman, Sampling Consultant to Anglo Platinum, which mainly affected the sections of the documents related to sampling, and which were incorporated into Release 2 of the Code and Guidelines. The content of Release 2 has undergone a comprehensive professional editing process during 2006, while and comments have been received from several of the sponsors’ operating companies, as well as from a number of experts in the field of sampling and of statistics. These have been incorporated into the documents, where appropriate, and mainly affect the Guidelines. Further contributions have also been received from the P754 Research Teams at the Universities of Cape Town, Queensland, and Stellenbosch and these have assisted the team preparing the Code and the Guidelines. During the drafting of these documents, regular discussions were held with representatives of the South African ing Profession and their suggestions and comments have been incorporated. On behalf of the of the P754 Code Development Team, I would like to express our sincere thanks to all those listed above for their help in compiling the Code of Practice and the Guidelines. This release is an update of Release 2 which was seen as a trial or ‘beta’ version of what we hope will become a set of documents which can be applied throughout the minerals industry to assist operating management in the design, installation and operation of sound metal ing systems, which will meet the requirements of corporate governance. To achieve this, Release 3 of this Code of Practice and Guidelines must be introduced and applied in the operating environment of companies, so that any flaws in the documents can be identified and eliminated as rapidly as possible. In addition to the sponsors of P754, Release 3 will also be distributed to people who have expressed interest in participating in the evaluation of the documents, and to a wider group of potentially interested parties at workshops provisionally scheduled to be held in Australia and South Africa during 2007. A further review and revision of the documents is planned for the end of 2007, after which Release 4 is expected to be distributed to a wider group of interested parties, including mining companies in North America, during the first quarter of 2008. Following the distribution of Release 4, it is hoped that sufficient interest in the documents and for them will have been generated to justify the establishment of an international coordinating body, through the appropriate AMIRA P754: Code Release 3: 20070219 Commercial in Confidence

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professional societies, to maintain the documents and incorporate further improvements as they are identified, so that the Code of Practice and the Guidelines can become living documents and can be accepted as a basis for Metal ing in the global minerals industry. We look forward to your and comments, which should be addressed to Ms Tess Chalk, AMIRA International Africa, at [email protected]

Peter Gaylard Convenor, P754 Code Development Team 2007-02-19

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Code of Practice for Metal ing Developed in of the AMIRA P754 Project: Metal ing and Reconciliation

Preface to the Code on Metal ing Historically, in the Mining and Metallurgical industry, the ing for metals/commodities across processing plants has not generally been performed at a level required by currently acceptable corporate governance practices. A number of factors have contributed to this, primarily a lack of knowledge and understanding of the principles of mass balancing and metal ing. However, other factors have also played a major role; for example, the equipment required to achieve an acceptable level of ability is often a last minute addition to a new plant design and as a result, it can be inadequately designed and installed. In addition, the installed equipment is often poorly maintained and operated. Capacity and staffing of laboratory facilities are also frequently insufficient to allow for the accurate and timely reporting of metal ing results. These drawbacks, together with the need for metal ing to be conducted to the level of “financial ing” standards with respect to auditability and transparency, for example, have been recognised for some time. Following the symposium entitled “Challenges in Metallurgical ing & Information Management” held in Cape Town in August 2001 under the auspices of the SAIMM, several interested parties held discussions on how the situation could be improved. The outcome of these discussions was a proposal for a project to be coordinated by AMIRA International entitled “P754 – Metal ing and Reconciliation” which was subsequently accepted and sponsored by Anglo American, Anglo Platinum, BHP Billiton, Kumba Resources and Rio Tinto. The objective of the project was to develop a Code of Practice for Metal ing, which would be similar in concept to the JORC and SAMREC Codes produced for the reporting of mineral resources and ore reserves. At the same time, research projects were established at several universities in South Africa and Australia, which, in collaboration with industry, will aim to provide solutions to some of the more intractable aspects of metal ing. A team under the leadership of Professor Peter Gaylard, UCT, was commissioned in August 2004 to develop the Code, which focuses on the need for all activities in the areas of mass measurement, sampling, sample preparation and analysis to be carried out accurately and with an acceptable level of precision. In addition, wherever possible the Code recommends the use of International Standards as the basis for all evaluation activities. Calculations and reports must be on a full “Check in-Check out” basis and must be prepared in a completely transparent manner with clearly defined audit trails. During its development, the Code has been discussed on many occasions with representatives of the financial auditing profession, who have offered many helpful suggestions on its content and who are in general agreement with its aims.

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Although it is accepted that the Code will be a living document, it has now reached a stage where it can be endorsed for use by the Industry. At the heart of the proposed Code are the following ten Principles: 1

The metal ing system must be based on accurate measurements of mass and metal content. It must be based on a full Check in-Check out system using the Best Practices as defined in this Code, to produce an on-going metal/commodity balance for the operation. The system must be integrated with management information systems, providing a one-way transfer of information to these systems as required.

2

The system must be consistent and transparent and the source of all input data to the system must be clear and understood by all s of the system. The design and specification of the system must incorporate the outcomes of a risk assessment of all aspects of the metal ing process.

3

The ing procedures must be well documented and friendly for easy application by plant personnel, to avoid the system becoming dependent on one person, and must incorporate clear controls and audit trails. Calculation procedures must be in line with the requirements set out in this Code and consistent at all times with clear rules for handling the data.

4

The system must be subject to regular internal and external audits and reviews as specified in the relevant sections of the Code to ensure compliance with all aspects of the defined procedures. These reviews must include assessments of the associated risks and recommendations for their mitigation, when the agreed risk is exceeded.

5

ing results must be made available timeously, to meet operational reporting needs, including the provision of information for other management information systems, and to facilitate corrective action or investigation. A detailed report must be issued on each investigation, together with management’s response to rectify the problem. When completed, the plan and resulting action must be signed-off by the Competent Person.

6

Where provisional data has to be used to meet reporting deadlines, such as at month ends when analytical turn-around times could prevent the prompt issuing of the monthly report, clear procedures and levels of authorisation for the subsequent replacement of the provisional data with actual data must be defined. Where rogue data is detected, such as incorrect data transfer or identified malfunction of equipment, the procedures to be followed together with the levels of authorisation must be in place.

7

The system must generate sufficient data to allow for data verification, the handling of metal/commodity transfers, the reconciliation of metal/commodity balances, and the measurement of accuracies and error detection, which should not show any consistent bias. Measurement and computational procedures must be free of a defined critical level of bias.

8

Target accuracies for the mass measurements and the sampling and analyses must be identified for each input and output stream used for ing

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purposes. The actual accuracies for metal recoveries, based on the actual accuracies, as determined by statistical analysis of the raw data, achieved over a company’s reporting period must be stated in the report to the Company’s Audit Committee. Should these show a bias that the Company considers material to its results, the fact must be reported to shareholders. 9

In-process inventory figures must be verified by physical stock-takes at prescribed intervals, at least annually, and procedures and authority levels for stock adjustments and the treatment of uned losses or gains must be clearly defined.

10 The metal ing system must ensure that every effort is made to identify any bias that may occur, as rapidly as possible and eliminate or reduce to an acceptable level the source of bias from all measurement, sampling and analytical procedures, when the source is identified. It is possible that, in older metallurgical operations, it may be physically impossible or very expensive to implement all of the above principles, in which case the Code stipulates that an Exception Report must be submitted for approval by a Competent Person as well as by management. In addition to those already mentioned, the Code covers areas such as Safety, Training, Data Management and Design of Metal ing Systems. Two further very important sections cover the appointment of a Competent Person to coordinate implementation of the Code and the requirements for internal and external audits of the Metal ing system. The recipients of the Code and its attachments should include independent Directors, of the Risk/Audit Committee, senior corporate and operational management, project design staff (including outside contractors) and plant operation and laboratory staff.

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Disclaimer The attached Code, including its ing Guidelines, (the “Code”) has been developed by and on behalf of certain sponsor companies and organisations (the “Sponsors”) pursuant to the AMIRA P754 Project on Metal ing and Reconciliation. While it is the intention of AMIRA and the Sponsors that the Code (or subsequent revisions of the Code) will ultimately achieve global and industry wide acceptance as a code of best practice in relation to metal ing, the following points should be noted: •

The Code has not at this stage been formally adopted, for internal purposes, by any of the Sponsors or by any industry associations and hence it is not binding on any such companies, organisations, associations or on any directors, officers, agents, employers, advisors or thereof

•

The Code has not been adopted by any legislative or regulatory bodies (including any stock exchanges), nor has it been incorporated into the listing or other rules of such exchanges

•

The Code does not purport to be, nor should it be construed as constituting, any form of legal or other standard in relation to metal ing

•

Neither AMIRA, nor the Sponsors nor any of their directors, officers, agents, employees and advisors make any representation or warranty regarding, nor shall they have any liability whatsoever in respect of any statement, recommendation, advice, opinion or information contained in the Code

•

To the extent that the Code is made available to third parties by or on behalf of the Sponsors, such action (or any subsequent interactions) shall not be construed as giving rise to any contractual relationship or any duty of care, of whatsoever nature, between AMIRA, the Sponsors (and/or their directors, officers, agents, employees or advisors) and such third parties.

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TABLE OF CONTENTS Page

Preface to the Code on Metal ing

iii

Disclaimer

vi

TABLE OF CONTENTS

vii

1

INTRODUCTION .................................................................................1

1.1

SAFETY AND HEALTH

2

1.2

PRINCIPLES OF METAL ING

2

1.3

STANDARDS

3

1.4

TRACEABILITY

3

1.5

ACCURACY AND PRECISION IN METAL ING

4

1.6

ERROR TYPES

4

1.7

METAL ING RISK

4

1.8

TRAINING

5

2

DATA MANAGEMENT........................................................................6

2.1

INTRODUCTION

6

2.2

DATA ACQUISITION / ENTRY

6

2.3

DATA DOCUMENTATION

6

3

MASS MEASUREMENT .....................................................................7

3.1

INTRODUCTION

7

3.2

ACCURACY AND PRECISION OF MASS MEASUREMENT

8

3.2.1

Scales / Weighing Equipment

8

3.2.2

Flow Meters

9

3.3

METHODS OF MEASURING MASS

9

4

SAMPLING........................................................................................ 11

4.1

INTRODUCTION

11

4.2

ACCURACY AND PRECISION OF THE SAMPLING SYSTEM

11

4.3

MULTI-STAGE SAMPLING SYSTEMS

12

4.4

SAMPLE STORAGE

12

4.5

SAMPLE LABELLING

12

5

SAMPLE PREPARATION AND ANALYSIS ..................................... 13

5.1

INTRODUCTION

13

5.2

SAMPLE PREPARATION

13

5.2.1

Moisture determination

13

5.2.2

Sample Reduction

13

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5.2.3

Sample Mixing

13

5.2.4

Sample Division

14

5.2.5

Sample Storage After Sample Preparation

14

5.3

ANALYSIS

14

6

STOCK-TAKES................................................................................. 15

7

METAL BALANCING........................................................................ 17

7.1

INTRODUCTION

17

7.2

THE METAL ING PROCESS

17

7.3

METAL BALANCING APPLICATIONS

19

7.3.1

Concentrators with minimal accumulation

19

7.3.2

Concentrators with intermediate accumulation

19

7.3.3

Coal treatment plants

19

7.3.4

Pyrometallurgical plants

19

7.3.5

Hydrometallurgical plants

20

7.3.6

Heap leaching

21

7.3.7

Bench scale, pilot and demonstration plants

22

7.4

MASS BALANCING FOR ENVIRONMENT AND HEALTH MONITORING

23

7.5

MINE TO PRODUCT BALANCING AND RECONCILIATION

23

8

REPORTING ..................................................................................... 24

9

SELECTION AND USE OF A COMPETENT PERSON .................... 25

10

DESIGN OF METAL ING SYSTEMS .............................. 26

11

METAL ING AUDITING RULES...................................... 27

11.1

INTRODUCTION

27

11.2

AUDIT TEAM AND ITS RESPONSIBILITY

27

11.3

METAL ING REVIEW

27

11.4

PHYSICAL AUDITS

27

CODE GLOSSARY

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1

INTRODUCTION

This Code document has been developed as one of the deliverables of the AMIRA P754 Project on Metal ing and Reconciliation and it is ed by a Metal ing Guidelines Document, to which are attached Appendices containing glossaries of relevant technical , lists of references, and illustrative examples of metal ing procedures. The Guidelines Document is intended to serve as a manual for plant operating and metal ing personnel. Further deliverables of the P754 project are the outcomes of site based research projects on metal balancing and reconciliation, which will be incorporated in the Guidelines Document as they become available. Where words are highlighted in bold lettering in the Code, their definitions in of Metal ing are included in the Glossary to this Code Document. This document is intended to serve as a Metal ing Code of Practice, which will become the provenance of suitable groups of industry organisations, as a way to gain broad industry acceptance worldwide as the Industry Code for best practice in metal ing. It should also be regarded as a living document, which will be revised through contributions from sponsoring companies, the related research program and other initiatives. Metal ing is defined as the system whereby selected process data (pertaining to metals of economic interest), including mass measurement and analysis are collected from various sources and transformed into a coherent report format that is delivered in a timely fashion in order to meet specified reporting requirements. A Metallurgical (Metal) balance involves the determination, through measurement, analysis and computation, of the magnitude of each component of interest across an entire process (Primary ing) or across each section of a process flow sheet (Secondary ing). A metallurgical balance, which is suitable for reporting of Metal ing, should be supplemented by defined uncertainties associated with each determination. In of this Code, a metallurgical (metal) balance encomes metals, minerals or commodities of interest, which can also include species which are measured and balanced for purposes of reporting on the environmental compliance of an operation. The ultimate purpose of the Code is to define the best practice for creating and reconciling process metal balances, although it does not prescribe specific valuation methods, as these will be dependent on the practices and policies of the company or operation concerned. The Code is based on a set of metal ing principles, which are set out in detail in Section 1.2. These incorporate the use of the Check in-Check out procedure for metal balancing, as well as the use of Exception Reports for reporting all deviations from the Code. These reports, in turn, have to be signed off by the Competent Person before being submitted to the Audit Committee of the company for ratification. The Competent Person is also required to approve the final design and installation of the metal ing system and to conduct routine audits of its operation.

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The Code document sets out principles for use and application by operating management for obtaining credible figures, such as the methods to be used and their bias and precision, and they should facilitate full disclosure of operational performance, including losses in emissions, in line with requirements for triple bottom line reporting in financial statements. At present, the Code does not address metal ing in mines and open cast operations, as it takes the feed to metallurgical treatment plants as its starting point. It is envisaged that this aspect will be addressed as the Code is developed. However, mass measurement in stopes and ore blocks is discussed briefly.

1.1

SAFETY AND HEALTH

The safety and health of staff involved in Metallurgical ing activities must always be of primary importance. Staff must, therefore, be well trained, provided with the necessary personal protection equipment and be fully aware of the company’s safety and health policies and procedures. In addition, access to equipment for inspection, cleaning and maintenance must be safe, with the appropriate work practices, lockouts and permissions in place.

1.2

PRINCIPLES OF METAL ING

The basic principles of metal ing are summarised below: 1

The metal ing system must be based on accurate measurements of mass and metal content. It must be based on a full Check in-Check out system using the Best Practices, as defined in this Code, to produce an on-going metal/commodity balance for the operation. The system must be integrated with management information systems, providing a one-way transfer of information to these systems as required.

2

The ing system must be consistent and transparent and the source of all input data to the system must be clear and understood by all s. The design and specification of the system must incorporate the outcomes of a risk assessment of all aspects of the metal ing process.

3

The ing procedures must be well documented and friendly for easy application by plant personnel, to avoid the system becoming dependent on one person, and must incorporate clear controls and audit trails. Calculation procedures must be in line with the requirements set out in this Code and must be consistent at all times with clear rules for handling the data.

4

The system must be subject to regular internal and external audits and reviews as specified in the relevant sections of the Code, so as to ensure compliance with all aspects of the defined procedures. These reviews must include assessments of the associated risks and recommendations for their mitigation, where the agreed risk is exceeded.

5

ing results must be made available timeously, to meet operational reporting needs, including the provision of information for other management information systems, and to facilitate corrective action or investigation. A

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detailed report must be issued on each investigation, together with management’s response to rectify the problem. When completed, the plan and resulting action must be signed off by the Competent Person. 6

Where provisional data has to be used to meet reporting deadlines, such as at month ends when analytical turn-around times could prevent the prompt issuing of the monthly report, clear procedures and levels of authorisation for the subsequent replacement of the provisional data with actual data must be defined. Where rogue data is detected, such as incorrect data transfer or identified malfunctioning of equipment, the procedures to be followed together with the levels of authorisation must be in place.

7

The system must generate sufficient data to allow for data verification, handling of metal/commodity transfers, reconciliation of metal/commodity balances, and measurement of accuracies and error detection, which should not show any consistent bias. Measurement and computational procedures must be free of a defined critical level of bias.

8

Target accuracies for the mass measurements and the sampling and analyses must be identified for each input and output stream used for ing purposes. The actual accuracies for metal recoveries, based on the actual accuracies, as determined by statistical analysis of the raw data, achieved over a company’s reporting period must be stated in the report to the Company’s Audit Committee. Should these show a bias which the Company considers material to its results, the fact must be reported to shareholders.

9

In-process inventory figures must be verified by physical stock-takes at prescribed intervals, at least annually, and procedures and authority levels for stock adjustments and the treatment of uned losses or gains must be clearly defined.

10 The metal ing system must ensure that every effort is made to identify any bias which may occur as rapidly as possible and to eliminate or reduce to an acceptable level the source of the bias from all measurement, sampling and analytical procedures, once the source has been identified.

1.3

STANDARDS

Wherever possible, procedures used in Metal ing must be based on the appropriate International or National Standards.

1.4

TRACEABILITY

In the areas of mass measurement and analysis, the aim must be to establish traceability back to the relevant international standard units of mass, distance and time.

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ACCURACY AND PRECISION IN METAL ING

1.5

All of the operations and equipment used to derive data for the reporting of Metal s, which include mass measurement, sampling, sample preparation and analysis, must be designed and conducted to achieve an accurate result. The actual accuracy of the independent operations involved in obtaining data for the Metallurgical s must be measured on at least an annual basis or, if the nature of one of the measurement parameters changes, e.g. particle size of the process stream, sampler design, analytical method etc. The precision of mass measurement, sampling and analyses must be monitored on an on-going basis by a robust statistical method and an investigation must be commenced when the monitoring system indicates that the precision is outside the acceptable range. The overall expected precision of the Metallurgical s must be calculated from these values and compared to the actual metal imbalance.

ERROR TYPES

1.6

Errors in measurement can be classified into three types: •

Random Errors

•

Systematic Errors

•

Illegitimate (Spurious) Errors

The value of the random errors must be minimised by careful operation and the causes of systematic and illegitimate errors identified and eliminated.

1.7

METAL ING RISK

Any production operation is exposed to financial risk associated with its Metal ing process to the extent that there is uncertainty in the evaluation of the quantities of metal delivered to that operation and produced by it. In addition, there is the risk to the reputation of a company and of negative market perceptions resulting from major Metal ing discrepancies. One of the prime objectives in the design of a Metal ing system is to enable an operation to quantify, manage, and minimise the level of risk to which it could become exposed, through failures or shortcomings in its Metal ing system. Each operation must conduct an initial Risk Assessment of its entire Metal ing system, to establish and quantify the risks. The design of a Metal ing system must take these risks into consideration and set down procedures to be followed when ing discrepancies are identified. The use of statistical analysis, and check measurements and analyses provides a database, which becomes an essential component of the risk management process. The other fundamental component of the risk management process is regular and frequent management reviews, incorporating Risk Assessments, of the entire Metal ing system, ed by both internal audits and external audits carried out by the Competent Person.

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The main areas of risk to which an operation could be exposed in its Metal ing system are:

1.8

•

Misrepresenting the performance of the operation through incorrect weighing, sampling or analytical procedures; inaccurate weighing or sampling equipment; incorrect stockpile estimation; incorrect calculations or the use of arbitrary calculation methods; poor control of ing samples and the utilisation of illegitimate or unacceptably biased data.

•

Real losses of an unknown nature, such as theft of material, through failure to identify and investigate ability problems promptly.

TRAINING

There must be full training procedures available to staff in line with International and National Standards together with appropriate sections of the Metal ing Guidelines. The level of training and qualification requirements of staff will obviously vary depending on their role in the ing system.

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2 2.1

DATA MANAGEMENT

INTRODUCTION

The operation of a Metal ing System represents the accumulation and evaluation of process data (e.g. mass measurements and analyses) from various sources. The acquisition, processing and management of data require a substantial portion of the effort associated with Metal ing. Efficient, secure and reliable data management adds considerable value to the Metal ing System. The goal of data management is to create data files from data obtained from various plant sources in such a way as to prevent systematic and spurious error. Production (e.g. mass measurement) and laboratory data (e.g. analyses) need to be integrated to a single interface to facilitate Metal ing. At the same time, however, it is essential that those responsible for entering plant operating data, and those responsible for entering analytical results into the system, are able to operate independently of each other.

2.2

DATA ACQUISITION / ENTRY

There are a number of ways of acquiring or entering information for use in a Metal ing System. Data may be acquired from various plant systems such as SCADA, Plant Historians, LIMS systems, etc. Manual entry of data to operator log sheets and transposition to spreadsheets is common practice on some plants. Spreadsheets do not allow for controlled data entry and they are easily corrupted. It is easy to inadvertently type over data, lose track of records, duplicate and improperly enter data in a spreadsheet. Further to this, the justification of formulae in spreadsheets often lacks clarity, and is not always recorded with the spreadsheet. Because of this, the justification can get lost, especially if the developer of the spreadsheet moves on, thereby making spreadsheets vulnerable to error and unjustified computations. For these reasons spreadsheets must be avoided as a method of data entry.

2.3

DATA DOCUMENTATION

Metal ing systems must include sufficient documentation of the data (metadata) that is being utilised for the various calculations so as to provide a clearly defined audit trail.

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3

MASS MEASUREMENT

INTRODUCTION

3.1

The overriding objective of mass measurement for Metal ing purposes is to establish the mass of the particular material or component present at a specific time, or the mass flow of that component over a defined time period, to a defined accuracy suitable for mass balancing, using procedures that take of Best Practices. At the same time, the cost/benefit relationship, the difficulty of measuring and sampling the process stream and the importance of the measurement to the Metal Balance must also be taken into consideration. In of mass measurement, accuracy may be defined in several different ways, such as maximum percentage error of reading, or maximum percentage error at full scale of the instrument or equipment. It is critical to ensure that the definition of the levels of accuracy in supplier’s documentation or the literature is comparable with those used in this Code and in the applicable Standards. A fundamental requirement for all mass or volume measurements is traceability back to the accredited standards used for calibration and certification. Mass measurement can be classified into three areas: •

Those measurements necessary for Custody Transfer and Primary Metal ing

•

Measurements required for Secondary Metal ing, and management control including intra company transfers and plant area performance, which may also be used for cross checks on Primary ing. This also includes those measurements necessary for environmental monitoring

•

Measurements required for plant control only

This Code is concerned only with the requirements for the measurements related to Primary ing. The requirements for accuracy of the measurement are the most stringent for the first area above requiring certified (assized) weighing equipment and the ability to establish the distribution of the random errors associated with the measurements. In all weigher and flow meter installations, it is essential that the selection, system design, installation, operation, maintenance and calibration follow the applicable standards and manufacturers’ recommendations, in order to achieve the accuracy and precision levels quoted in Section 3.2, as well as those required by International and National Standards and National Metrology Acts. All the data from the checks and calibrations must be collected and stored to enable statistical analysis and assessment, to establish if bias exists and to calculate the variance and likely error in the value recorded.

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3.2

ACCURACY AND PRECISION OF MASS MEASUREMENT

3.2.1 Scales / Weighing Equipment In the weighing industry, a scale is accurate if it has been properly calibrated, it conforms to the applicable standard, and its performance is within the permitted tolerances (or maximum permissible errors) for initial verification and in service. Although the maximum tolerances specified in the Standards (such as NIST 44) may suggest 95% confidence levels, this is not actually stated. Accuracy is generally expressed in the industry in of “inaccuracy” or the amount of error, and is quoted as ± x%, usually of the full-scale value, without defining the confidence levels. The precisions quoted in this section are percentages of full-scale value. Often the number quoted in literature is actually precision and not accuracy. For further discussion see the Guidelines. The figures quoted below are expected precisions in of a coefficient of variation. Static scales provide the most accurate and precise method of measuring mass and must be the choice, where circumstances permit, for Custody Transfer and Primary and Secondary Metal ing purposes. Certified in-motion weighing is also a suitable choice for Primary Metal ing but for rail transport only. Certified electromechanical belt weighers must be the second choice for metal ing purposes after static scales, if static weighing is not feasible. Providing proper installation, operation and calibration are adhered to, precisions obtainable generally range between ± 0.05% to ± 0.2% for platform scales and between ± 0.1% to ± 0.5% for weighbridges on gross load measurements. For hopper/bin scales and gantry scales the equivalent figures are ± 0.1% to ± 0.25% and ± 0.15% to ± 0.4% respectively. For in-motion weighing, provided that the key principles of taring each truck or hopper on each occasion and controlling the truck speed are followed, a precision of ± 0.5 % is feasible. A precision of ± 0.5% should be possible for conveyor belt weighing on most applications, providing all the guidelines for selection, design, installation, operation and calibration are followed. Nuclear belt weighers must not be used for Primary Metal ing but they can be used for Secondary Metal ing if the material is homogeneous in size and composition and the belt loading is uniform. Mass measurement equipment will measure the wet weight, therefore, accurate determination of the moisture content is essential.

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3.2.2 Flow Meters The accuracy of flow meters can be expressed in three different ways: •

Percentage of span

•

Percentage of a rate or reading

•

Percentage of the upper range limit

The accuracies quoted below are those at 95% confidence levels. The accuracy of electromagnetic flow meters, especially on slurries, is installation specific. Typical accuracies are ± 0.2 to ± 1% of reading and ± 0.5% is attainable on water or clean solutions if selection, design, installation, operation and calibration follow the applicable standards and manufacturer’s recommendations. On slurries, however, the accuracy can be much worse and their use for Primary Metal ing in these cases is not recommended unless the use of static or belt weighing is not possible. Coriolis mass flow meters are capable of high accuracy, ± 0.2% of reading in certain cases, but they only have limited application in the extractive industry, as they require low fluid velocity and they are sensitive to the amount of entrained air. Ultrasonic transit time meters have limited applicability for specific applications and they can give an accuracy of 0.5 to 2% of the reading. Momentum (impact) mass flow meters can be used for free flowing solids and they are capable of achieving an accuracy of ± 1.0% of the reading. Differential pressure type meters are not recommended measurements, as there are too many cumulative sources of error.

for

ing

All methods utilizing a volume or volume flow determination require the measurement of the bulk density of the material in the stockpile/bin or of the specific gravity/relative density of the slurry or liquid. This can be performed by nuclear (gamma) gauge, or preferably by bulk or laboratory tests which are the best methods. In a few cases, such as for fine coal stockpiles, a penetrometer may be used. The density determination is often a cause of error in the mass measurement calculation.

3.3

METHODS OF MEASURING MASS

The various methods of measuring mass and volume, their advantages and disadvantages, as well as the applicable standards for design, installation, calibration, certification, operation and maintenance, and their suitability for use in Metal ing, are described in the Guidelines Section 3.3. Certain of these methods are prescribed for the various applications in Primary ing. The mass may be static in a tank, bin or stockpile or flowing on a conveyor belt, in a conduit or in an open channel, or it may be moving in railway or road trucks. Methods for measuring volume and volume flow, density and moisture are also covered in the Guidelines.

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Measurement of mass in stockpiles and process inventory is subject to the highest level of uncertainty. Use should be made of parallel streams, and stocks must periodically be reduced to zero. Specific procedures such as Draught Surveys may contractually apply to the mass measurement of bulk commodities. Methods for measuring mass in gaseous and liquid effluents are specialised and they are usually defined in environmental legislation. Measurement of mass in stopes or ore blocks is not covered, as it is outside the scope of this Code at present. Certification is required for all Primary ing mass measurement instruments. If after due assessment of the risk, certification is considered unwarranted subject to an evaluation of the: •

costs/benefits

•

company needs

•

practical limitations.

Then, suitable in-house procedures must be drawn up and applied, for the calibration, operation and maintenance of equipment. These procedures must be based on the appropriate standards and legal requirements and an Exception Report to the Code motivated and agreed.

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4 4.1

SAMPLING

INTRODUCTION

Correct sampling of a particulate material from a moving stream is achieved when the expected number fraction of the particles in the sample is equal to the average number fraction of the particles in the process stream during the time that it takes to extract the sample increment. To achieve this objective, the sampler must be correctly designed, installed and operated in accordance with sampling theory. This issue is dealt with in the Guidelines. A mechanically correct sampler is unbiased. Where possible, the correctness of the sampler and its freedom from a specified level of bias should be confirmed by carrying out bias tests by the stopped belt method or another suitable reference method of sampling. The sampling from stockpiles and bins for ing purposes must be avoided because of the great difficulty in obtaining an accurate sample. The sampling of other process streams/products must be carried out according to the available International Standards.

4.2

ACCURACY AND PRECISION OF THE SAMPLING SYSTEM

A mechanically correct sampling system cannot be biased in the long run. The most effective manner of ensuring that sampling systems are free of bias is to design them to be mechanically correct under all circumstances (material types and range of mass flow). If a sampling system cannot be designed in a manner that is unequivocally mechanically correct, provision must be made to allow accurate bias testing. If the nature of the flow being sampled changes, a sampler that is not mechanically correct must be tested for bias on the new material type. A correct sampling system can show a bias over a short period of time if the sampler takes increments at fixed time intervals, and this interval is a multiple of the period of a periodic variation in the mass flow or quality of the process stream. To eliminate this effect, the sampler must be operated in a random stratified sampling mode. Sampling systems should be designed to achieve a precision that has been determined according to the requirements of the metal ing system. To accomplish such a design, the process stream should be studied to determine the nature of the solids and liquids to be sampled and to determine the statistical variability of the measurands with time. When the samples are to be used to provide data to a metal ing system, the sampling accuracy and the accuracy of all subsequent sample preparation and analysis steps should be such that the overall accuracy is what is genuinely needed in that system. The only unequivocal method of establishing the precision of a sampling and sample preparation system is to ensure that the sampling system can carry out inter-leaved sampling.

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MULTI-STAGE SAMPLING SYSTEMS

4.3

In situations where, due to high mass flow rates of the process stream, or where the required number of increments to achieve a desired sampling variance produces an excessive sample mass, it is necessary to install a multi-stage sampling system. In such a system there will be a number of sampling stages that will reduce the final sample mass to a level that can be handled by the laboratory. The design criteria for multi-stage sampling systems must include the following:

4.4

•

Each sampling stage must employ a correctly designed mechanical sampler

•

There must be a storage system between each stage capable of holding the total increments from the previous stage

•

The feed rate between stages must be such that at least 30 increments are taken by the subsequent sampler

•

If there is particle size reduction in the system, this must be done to minimise additional sampling variance. The level of variance is dependent on the heterogeneity of the material, which in turn determines the sampling constant. Ideally, the variance must be established by experimentation on the material concerned. In certain cases, as an interim measure, it is possible to use a “Sample Safety Line” such as the one described by Gy, provided the limitations of this approach are clearly understood.

SAMPLE STORAGE

Prior to delivery to a laboratory, samples must be stored in suitable sealed containers, so that any change to their physical or chemical parameters is minimised. This is especially important with regard to their moisture content.

4.5

SAMPLE LABELLING

All samples must be labelled in such a way that their unique identity is unambiguous, using a system that can withstand normal handling.

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5 5.1

SAMPLE PREPARATION AND ANALYSIS

INTRODUCTION

All operations in sample preparation and analysis must be carried out to provide unbiased data, with a known and acceptable precision and within a time frame that is suitable for Metal ing purposes. Cross-contamination of samples during sample preparation must always be guarded against. Samples with known differences in concentration such as feeds, tails, concentrates, mattes and slags must be prepared in separately enclosed areas using dedicated equipment. With underground and geological samples, where large variations of grade are unavoidable and unknown, it must be established that there is minimal hold-up of material within the sample reduction equipment. Frequent cleaning of this equipment may be necessary.

5.2

SAMPLE PREPARATION

5.2.1 Moisture determination Moisture is defined as water which can be removed from the sample by drying to constant mass at temperatures up to 105 oC, as water can be present in the material in different forms including water of crystallisation. It is important to ensure that samples for moisture determination are taken in a manner that will ensure that the moisture in the sample is representative of the material being weighed and that no evaporation can take place, or water ingress occur, before the determination takes place. It is also very important to ensure that any analysis is carried out on the sample that has been used for moisture determination or, if a nominally identical sample is used, that an appropriate correction be made to convert the analyses from an as-received to a dry basis, in order that the analytical result can be related directly to the dry tonnes value.

5.2.2 Sample Reduction Sample Reduction must be carried out in equipment that does not contaminate the sample and in which dust losses are minimal. Sample Reduction ahead of Sample Division must always be done to a particle size that is known to minimise additional sampling variance.

5.2.3 Sample Mixing Sample Mixing is unnecessary during sample preparation provided that Sample Division is carried out using correctly designed rotating sample dividers (spinning rifflers). If mixing is carried out it must be done in sealed mechanical systems. Paper rolling must not be used, as it generally leads to segregation of the sample by particle size or particle density.

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5.2.4 Sample Division Sample Division refers to the process of sub-sampling a quantity of material that is itself a sample, or a partial sample, from a lot of material. Sample division is always accompanied by an increase in the uncertainty associated with the sample. The increase in variance is given by the following equation:

⎡ 1 1 ⎤ ∆σ 2 = a2 K s ⎢ ⎥ ⎣ Mss Ms ⎦ where: Ks = The sampling constant for the material a

= The sample assay

Mss = The mass of the sub-sample retained Ms = The mass of the sample before division

Sample division should be carried out wherever possible using a rotary sample divider (spinning riffler). This recommendation extends to the extraction of the analytical aliquot from the mass of sub-sample pulverised in the previous preparation step. The Incremental Division procedure is acceptable. Stationary rifflers must be avoided.

5.2.5 Sample Storage After Sample Preparation Samples must be stored in suitable sealed containers to prevent any physical or chemical change and to minimise the possibility of cross-contamination. If the storage period is to be greater than three months, storage under nitrogen should be considered.

5.3

ANALYSIS

Analysis of samples for Metallurgical ing must be carried out using methods that have been accredited to ISO 17025. It is likely that such methods will involve a twin-stream (parallel analysis) procedure and they are performed using extensive QA and QC systems and checks. The calibration of instruments should conform to best practice and, where appropriate, it should comply with the requirements of VAM document LGC/VAM/2003/032. It is expected that there will be frequent use of certified reference materials and that an Umpire Laboratory will be used on a regular basis.

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6

STOCK-TAKES

Physical stock-takes, the determination of the Plant Stocks and the total Process Inventory are all integral to metal ing. The calculation of the dry mass and content of process inventory held in stockpiles, bins, tanks or other process vessels is subject to a high degree of uncertainty, mainly due to the difficulty in obtaining a representative bulk density and sample. It can also be difficult to make estimates of the volume of material that comply with the standard of accuracy needed for metallurgical ing. The effort put into the estimation of a particular item of stock within the plant should be dependent on the values of the contained balance species relative to the total input of all species that are expected to be found in the item of stock, taking due of the accuracy with which the balance is to be carried out. A great deal of reliance is placed by external financial auditors on “setting the clock to zero”; i.e. a full stock-take at regular intervals. The frequency with which stock-takes or audits of the plant operation are carried out is a management decision. It will relate to the value of the plant throughput, the inprocess inventories relative to the expected profit at year-end, and the cost of execution; however, stock-takes must be conducted on at least an annual basis. Stock-takes may be performed more frequently for plant control and these require a lesser degree of accuracy than those used in financial reporting. The latter stocktakes must be performed in conjunction with the company’s auditors. The cost of executing the stock-takes increases with the demand for accuracy of the result. The level of financial risk associated with a particular strategy will determine the final decision regarding stock-take frequency and accuracy. The costs incurred should be offset against the benefits of faster access to more accurate information relating to production performance and stock shortages or excesses. The overall confidence level in the result of the stock-take must be established from the uncertainties in the component measurements and it must be reported with the results. Having developed a stock-take policy, the procedure must be documented to ensure a clear definition of roles and responsibilities leading to the rapid and effective execution of the stock-take and the reporting of results. Procedures for handling differences between book and measured stocks must be fully documented. Any adjustments must be made only after the appropriate authorisation, as defined in this Code and the Guidelines. It is readily demonstrated that in-process stocks and draw-down or build-up of stockpiles are likely to represent the largest uncertainties in a Metal Balance. The most successful strategy for the elimination of these uncertainties is to use a policy of building parallel stockpiles, one of which can be exhausted at the end of the ing period. If stocks are never exhausted, measurement uncertainties will accumulate to a point where the balances will be essentially meaningless. The policy of eliminating stockpiles on a regular basis must be built into the general processing strategy. In cases in which the in-process inventory has a value that is large compared to the value of the net input for the ing period, the ing strategy must involve

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an operating policy, which permits the exhaustion of the in-process inventory immediately prior to the stock-take, such as the use of the Bubble Method.

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7 7.1

METAL BALANCING

INTRODUCTION

Metal ing is critical for corporate governance in monitoring, checking and improving performance and in identifying and quantifying losses and emissions. A satisfactory metal balance provides a convincing demonstration that: "What comes into the operation, less any internal accumulation, plus any draw down from internal stock, is sufficiently similar, i.e. within acceptable limits in mass and analysed components, to what goes out as product(s), losses and rejects". (Check in-Check out) Using the Check in-Check out system, all streams into and out of the Process or Plant for which the balance is being performed, are measured, sampled and analysed. In of this Code, this is the standard procedure to be adopted for Metal ing in all operations, and all deviations from this approach must be justified in an Exception Report approved by company management. The accuracy for each component measured is quantified and the resulting accuracy of the metal balance calculated and reported in order to determine whether it is within the acceptable limits, as defined for the operation by the company’s management. Applying the Check in-Check out system will normally show a difference between net input and output, due to measurement, sampling and analytical uncertainties, and this imbalance is treated as an uned gain or loss. The handling of this uned quantity and techniques for the reconciliation of the data and the detection of errors and bias are covered in the Code Guidelines (Section 7) with the essential overriding principles that any procedure or adjustment must be transparent, fully documented and approved. The primary purpose of the reconciliation techniques is to assist in pinpointing problem areas, thereby leading to improvements in applying the Check in-Check out system.

7.2

THE METAL ING PROCESS

Metal ing involves the recording, to ing standards, of information relating to the flow and transfer of materials and their contained metals, while the term “to ing standards” includes criteria such as accuracy, integrity, security and auditability which must be met. In turn, auditability requires that all source data must be traceable and all changes to the data must be authorised and recorded, together with reasons for the change and they must be filed to provide an audit trail. Metal ing is based on an ing period (typically one month), which is usually determined by company reporting requirements, but it could also be determined by specific reporting requirements for the operation concerned. Primary ing, the focus of this Code, relates to the balance across an entire plant; whereas Secondary ing involves balances across sections of the plant or individual processes. Secondary ing, therefore, can be used to help identify where process inventory is occurring, where time lags are involved, or where any measurement problems exist, thereby improving Primary ing.

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For custody transfers of material from one operation to another, it is common for the material to be weighed and sampled by both sender and receiver when the transfer takes place between corporate entities, as each entity has a requirement to complete its own metallurgical ing process. Although the mass measurements, sample(s) and analyses used for settlement purposes will be defined in the commercial agreement covering the transfer. When the transfer takes place between business units of the same corporate entity, the double weighing, sampling and analysis (Home and Away) system presents opportunities to monitor mass measurement, sampling and analysis variances and bias. If there is no detectable bias, all results should preferably be used to arrive at the values used for metal ing. If a bias is detected, the source of the bias must be investigated immediately and eliminated. Such a double check system is, however, expensive, and the resources might be better spent ensuring that the sampling at a single point is beyond reproach and that sample preparation and analysis is done to the highest standard, incorporating both appropriate quality assurance and control procedures. Evaluation involves activities, some technological and some statistical, concerned with the technical quality of the accepted data and it normally involves the comparison of two independent measurements of the same quantity. Recovery and ability are separate but related parameters, the determination of both being necessary for the efficient monitoring, over defined boundaries, of the performance of metallurgical plants or processes, as well as for reliable forecasting and planning. Although other definitions for Recovery and ability exist, which are currently used in the industry, as shown in the Guidelines for this Code (Section 7), the definition in this Code is Actual Recovery, which is calculated on the basis of all process inputs according to the formula: Actual Recovery (%) = (sum of all useful or desired outputs) x 100 (sum of all inputs) ability is defined by the following formula: ability (%) = (sum of all outputs + stock and inventory change) x 100 (sum of all inputs) This provides a check whether over time, inputs, outputs and stock changes reconcile. The day-to-day data collected from a metallurgical plant is rarely consistent and it will usually contain redundant information. In general, any two methods of calculation will yield different results and, therefore, many factors influence the selection of a suitable metal ing strategy. These factors are discussed in the Guidelines and some practical strategies for different types of metallurgical operations are indicated.

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7.3

METAL BALANCING APPLICATIONS

7.3.1 Concentrators with minimal accumulation As there is no significant accumulation of, or change in, process inventory in this type of concentrator, ability is measured by comparing all outputs with all process inputs over the ing period, ignoring any change in Plant Stock or Process Inventory. Care must be taken to ensure that the balance is not compromised by recycle streams and the return of plant spillage to the circuit, especially if these are of higher or lower grade than the main stream. Typically, in concentrators, it is possible to obtain reliable measurements of the feed and concentrate masses, but it is usually more difficult to measure the mass of the tailings stream. Thus, in some concentrator metal ing balances, Check inCheck out is not carried out and the masses of the feed and concentrates, together with the analyses of the feed, products and tailings streams, are used applying the two-product or three-product formulae. This practice must be motivated and approved via an Exception Report.

7.3.2 Concentrators with intermediate accumulation Where significant accumulation or changes in plant stocks or in-process inventory occur, the determination of the concentrator ability has to take this into as per the ability formula. The potential impact of the measurement of plant stocks and in-process inventory on the overall metal balance has been discussed in Section 6.

7.3.3 Coal treatment plants In the coal industry, the practice of metal/commodity ing, on a full Check inCheck out basis, is not rigorously practised, in the sense of tracking a valuable component in the plant feed/ore to a final product containing an enhanced concentration of that valuable component. However, there are stringent requirements regarding the mass measurement, sampling, and chemical and sizing analysis of coal products as contained in various International Standards (Guidelines Appendix D.2.2.) Operating performance is monitored through the determination of the Yield of product coal from the ROM material fed to the plant.

7.3.4 Pyrometallurgical plants Recovery and ability for smelters and pyrometallurgical plants are calculated using the same formulae as shown in Section 7.2. For Check in-Check out, all input and output streams around the plant must be measured and sampled leading to the calculation of the material balance closure errors for the various species. These balance closure errors reflect the material that is uned. However, dust and gas losses are normally subject to a high level of uncertainty and slag quantities are often not accurately measured, thus Check in-Check out cannot be carried out for AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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species affected by such losses and lack of measurement. Slags are not homogeneous; they may have metal or alloy inclusions and they may contain varying amounts of moisture, especially in the case of water-granulated slags. Fortunately, the metal content in gases, dusts and slags is usually a small proportion of the feed and the effect of omissions on the balance less significant. The amount of effort put into improving the accuracy of measurement of dust and slag losses is dependent on their magnitude and value, the risk potential and the requirements for environmental reporting. However, it is recommended that furnace slag be weighed before disposal or recycling. Smelters represent a challenge in of metal ing because of the difficulty in measuring in-process inventories, intermediates and recycles, which are often significant, including material from ladles and refractory repairs and metal/alloy build up in furnaces. Furthermore, the materials in streams around furnaces and converters are coarse, highly heterogeneous, variable in grade and may be molten. They are, therefore, among the most difficult materials to sample. Nevertheless, the ing system must either treat these materials as in-process stock or it must recycle them out and back into the process on a Check in-Check out basis.

7.3.5 Hydrometallurgical plants Hydrometallurgical plants are usually relatively complicated, with multiple stages, many circuits for purification and upgrading and several recycle streams. In addition, Metal ing is often required for more components than in the earlier stages of processing; these components include multiple products, residues, impurities, reagents and effluents. As process streams can be made up of either, or both, solids and liquids (organic and/or aqueous), with both containing significant quantities of metals, the Metal ing system for such plants must make adequate provision for the sampling and mass measurement of all the components of each feed and output stream and it must allow for the measurement and assessment of continuous recycle streams and of process spillage, which may occur on a periodic basis. Check in-Check out is essential for the feed and product streams including liquid and gaseous effluents. The precise measurement for effluents is difficult but, usually, the mass content of these streams is relatively small. Mass balancing of the individual process steps and circuits (Secondary ing) is important for monitoring process performance and it can provide checks on the Primary ing. Hydrometallurgical plants usually have a significant process inventory in the form of metals in solutions and in solids, including that in solvent extraction/electrowinning circuits. Estimation of this inventory is complicated by the changing metal contents of the solid and liquid phases as they progress through the plant. Estimation has to incorporate tank level measurements, slurry relative densities as well as solution and solids analyses at each stage of the process. In plants treating high value metals, the process inventory estimate should be verified at prescribed intervals by physical stock-takes, using procedures such as the bubble method, to reduce the probability of major stock write-offs caused by undetected errors in the routine ing system.

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Hydrometallurgical plants are subject to losses of valuable materials in effluents and they also normally generate various solid residues, which will contain some metal values in both solid and solution. Besides the valuable constituents, it is necessary to measure, for, and report on various impurities and pollutants to ensure conformity with environmental regulations.

7.3.6 Heap leaching The heterogeneous (two-phase), non-steady state nature of Dump and Heap leaching, together with a protracted leach cycle (several months) and the consequent large process inventory all complicate metal balancing. The large inventory also means that the effect of input or other changes is not readily apparent and can be subject to long delays. However, the material treated is often of low grade/value and high mass, and this must considered when assessing the cost effectiveness of the Metal Balance methodology. Further to this, the fact that the process inventory includes unleached, partially leached, and fully leached ore, as well as solution with varying metal contents, and that a significant proportion of the metal is not recoverable, raises issues that are not encountered in other processes. The main concerns are the inventory of metal in the leach solution and the determination and prediction of the recoverable metal in the feed material. Often the Check in-Check out process cannot be used, especially for Dump Leaching, as the mass and analysis of the dump is not known to a sufficient degree of accuracy and it is impractical or not cost effective to measure the metal remaining in the heap independently at the end of the ing period. The metal remaining in the heap is then determined by subtracting the quantity that has leached into solution from the original estimate of what was deposited on the pad. If this practice is utilised it must be approved by means of an Exception Report. The principles in this Code thus apply to Heap Leaching only, as they are not possible to achieve for most Dump Leaching operations. Feed material must be weighed, sampled and analysed according to the Code and the material must be tracked to the specific pile or pad. Drained pregnant solution from each heap and the barren solution returned as irrigation to the heaps must be accurately measured by suitable flow meters and sampled for analysis. Reconciliation of these flows is important in order to provide a calculation of the solution inventory in the leach piles, the metal extraction from all the heaps/modules under irrigation and the total plant production. To perform this reconciliation requires estimates to be made of the amount of rainfall, evaporation and the solution content of each abandoned heap. Rainfall and evaporation must be based on actual site measurements over the ing period. The solution content of operating, abandoned and resting heaps must be periodically established by test work, although standard figures based on experience are often used. The metal content in the solution in the heaps is normally the largest proportion of the total solution inventory and it is usually equivalent to several months’ production. However, the metal ing is complicated further by the fact that a significant proportion of the metal is not recoverable. Thus, it is common practice for process control, to utilise the recoverable metal placed on the heap, rather than total metal, for mass balance and performance calculations. If recoverable metal, as opposed to AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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total metal, is being used for metal ing, this must be approved by means of an Exception Report. The recoverable metal in the specific material placed on the heap must be determined by suitable laboratory tests on representative samples, for example, using rolling bottle or shake tests, or pilot-scale column tests. These values are adjusted by factors, based on experience with older heaps, to reflect the predicted performance of an operating heap. The procedures and methodology for performing these tests and calculations must be rigorously designed, documented and monitored. Thus, the balance will reconcile the output metal in solution with the inputted recoverable metal and it will further give the recoverable metal still in the heap in both solid and solution forms. To achieve a Check in-Check out balance, when a particular pile/heap has reached the end of its life after leaching and elution has been completed, a closing balance must be performed and the heap sampled to check the amount of total and recoverable metal remaining. If the heap is to be left in place, this sampling should preferably be performed by mechanical drill/augur or sonic drilling. If it is to be moved, it must be sampled conventionally as it is moved. The metal that was not recovered must be removed from the plant inventory when a heap is abandoned. Sampling of heaps is subject to a high level of inaccuracy and due cognisance of this must be taken in the use of the information generated for stock evaluation and for use in predicting future performance. The metal balancing from the output leach solution through the purifying and refining steps to metal product is similar to that for Hydrometallurgical processes. General practice is to calculate the recovery of the metal finally produced, after the purifying/refining process, as a percentage of the metal placed on the heaps.

7.3.7 Bench scale, pilot and demonstration plants Metal ing for pilot plants must embody all the principles covered in other sections of this Code. The design and operation of the plant must allow for suitable equipment and procedures, in line with this Code, for mass measurement, sampling and analysis that will ensure that meaningful results can be obtained to give metal balances to a defined accuracy, in order to facilitate the decision process. Operating and sampling manuals are required.

•

All input and output streams must be ed for, i.e. Check in-Check out

•

ability must be better than 100% (± 5% at 95% confidence levels) for the product metals and 100% (±10%) for impurities, for the results to be utilised for economic and process decisions

•

As quantities are much smaller than a full-scale plant, static weighing on certified weighers only must be used to establish feed and product weights

•

Samples are a larger proportion of the process flows than in a full scale plant and the mass and metal content of the samples must be included in the metal balances as part of the Check in-Check out

•

Similarly, spillage must be collected, weighed, analysed, and ed for in the balance at the appropriate stage. It should not be returned to the circuit as it causes process disturbances. Spillage should be kept to a minimum

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•

Effluents must be collected, accurately weighed and sampled

•

Appropriate metal balancing methods must be used to calculate plant performance

The entire sampling frequency and plan must be statistically designed to yield meaningful results allowing for the fact that planned variations in operating conditions will occur and that performance of individual process steps is required.

7.4

MASS BALANCING FOR ENVIRONMENT AND HEALTH MONITORING

For metal balance purposes, measurement of streams from operating plants only is relevant. However, measurement and quantification of any dissolution of heavy metals and other contaminants (such as Acid Mine Drainage) and percolation of critical compounds into aquifers or watercourses, or gases emitted and dust blown from slag and tailings dumps is also necessary for corporate governance purposes. Methods of measurement, sampling, and analysis are defined by organisations such as the Environmental Protection Agency and the National Institute for Occupational Safety and Health in the USA, the World Health Organisation and other international or national bodies as well as in national Air Quality and Water Quality Acts, regulations and other legislation. Companies must adhere to the appropriate regulations and guidelines and/or approved worldwide standards.

7.5

MINE TO PRODUCT BALANCING AND RECONCILIATION

A common terminology used, especially in the case of precious metals, for assessing operating performance, is the ‘call’ system, in of which the amount of metal that is produced as final metal and in the residues/tailings is compared to the quantity delivered to the plant in the plant feed. This gives the plant call factor, which is usually expressed as a percentage, and it is a measure of the ability. In the same way, the measured mass of metal in the ore delivered to the shaft, or the ore dumping point, from the mining operations is compared with the estimated metal content in the ore mined, as determined by sampling and surveying, to give the shaft (or mine section) call factor, also expressed as a percentage. The combined plant and shaft call factors give the mine call factor for the operation. Typically, these call factors are calculated monthly and they form a useful means of monitoring discrepancies between the expected and actual mine output. However, the reconciliation of Metal ing back to stopes or ore blocks is outside the scope of this Code at present.

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8

REPORTING

The Metal ing system must generate suitable reports, over various time frames and ing periods, to provide a Metal Balance of the desired constituents timeously to Plant Management and to the Financial ants. The report must highlight inconsistencies, problem areas, and indicate the confidence levels for the reported data. The report must include tonnage and analysis figures for all metal ing streams, and often a comparison of performance figures against key performance indicators, and variance from the operating budget or business plan. The report must contain sufficient information to assist in diagnosing plant operating problems and for future production planning and project evaluation. To meet the above objectives, the report must be compiled by adequately trained personnel and it must use data which has been subjected to the minimum number of manual entries, with clear traceability to the sources of all inputs. Compilation of the report must be via the use of approved metal balancing procedures and no data may be adjusted without written authorisation from the responsible person. The report should also comply with International Auditing Standards as set out in IAS 2 and it must be signed off by a Metal ing Committee, or another suitable authority, for subsequent approval by the Audit Committee of the Company’s Board of Directors.

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9

SELECTION AND USE OF A COMPETENT PERSON

A Competent Person, for the purposes of this Code, must be a member of the relevant local professional registration authority, or any other statutory local or international body that is recognised by the relevant Code s, and the person should have a minimum of five years experience relevant to the type of metal or mineral under consideration and to the type of operation involved. The Competent Person must:

•

be formally appointed by the management of the company or the operation concerned to recommend standards for the setting-up and auditing of Metal ing reporting systems

•

accept responsibility for the final approval of the design of the Metal ing systems

•

be responsible for auditing the system once it has been installed, to assess compliance with the design specifications

•

conduct periodic reviews and audits of the system, at pre-determined intervals, to ensure continued compliance and, as required, to investigate problems which may occur

•

submit reports to management in regard to the compliance of the system and shall monitor the action plans periodically produced to remedy faults

The Competent Person must be independent from the operation concerned.

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10

DESIGN OF METAL ING SYSTEMS

The specification of the metal ing system must be based on a study that has the objective of quantifying the impact of the accuracy of process stream and process stock measurement on the accuracy of the overall metal balances. Ideally the study will estimate the probability distributions of the critical balance estimates, to permit a full view of the financial risks associated with the balances. The design and specification of the system must include the definition of all risks or consequences of a degradation of the accuracy of the metal/commodity balance process and it must specify the means by which these risks can be quantified. The metal ing system must comply with this Code and it must include the following:

•

A system for data acquisition and management

•

An appropriate metal balancing methodology

•

Reporting intervals, dates and timing, as well as reporting rules and procedures

•

Characterisation of the process streams involved in the balance, including the nominal mass flows of balance species, heterogeneity of the solids to be sampled from the stream and usual levels of process variability

•

Sample equipment, sampling frequency, sample preparation procedures, expected sampling and preparation accuracy and analyses required, as well as cross-references to the required analytical procedures and the accuracy of those analytical procedures

•

Methods of monitoring the data streams of analytical results that will ensure that the target sampling, preparation and analysis variances are maintained

•

The quality control and assurance systems of all areas of metal ing must be included in the ing system documentation

•

Mass measurement points, type of measuring device and accuracy required

•

Levels of authority for approval of the data and, where necessary, for handling and approving changes to the data

•

ing battery limits

•

Stock-take procedures and frequencies

The design of the ing system must form part of the design scope for new plants and it must include careful identification of the mass measurement and sampling points required to enable a reliable metal balance to be determined; it must also pay careful attention to the detail of their installation, calibration, maintenance and routine operation. Where existing plants do not meet the requirements of this Code, either steps must be taken to comply with these requirements, or non-compliance must be motivated and approved via an Exception Report. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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11 11.1

METAL ING AUDITING RULES

INTRODUCTION

A company must have metal ing rules and a procedure for auditing compliance with these. These rules are primarily technical to provide management information and to promote good Corporate Governance but they must also provide the quantities and the reconciliation necessary for the financial reports.

11.2

AUDIT TEAM AND ITS RESPONSIBILITY

A team to audit the metal ing system must be established, including a financial auditor and, periodically, the Competent Person. The responsibilities of the team are to:

•

obtain a fair and soundly based analysis of the Metal s

•

witness stock-takes, on at least an annual basis

•

mass flow measurements and analytical results

•

assess the accuracy, precision and reconciliation of metal ing data

•

report to suit the company’s reporting period

•

identify and advise on areas that are inadequate and recommend actions

•

provide a structure to the Competent Person

11.3

METAL ING REVIEW

On at least an annual basis, the metal ing system methodology must be reviewed by a team of suitably experienced staff, to ensure that it is still fulfilling its requirements. Should this review lead to recommendations for changes to the existing system, these must be approved by the Competent Person, prior to incorporation in the system.

11.4

PHYSICAL AUDITS

A series of physical audits must be implemented to ensure that all aspects of the metal ing chain are operating as specified. Obviously, the auditing of problem areas must be scheduled at a frequency to enable the problem to be positively identified and eliminated.

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CODE GLOSSARY ability – The ratio of the total output of a plant, or section of a plant, for a given material balance species (element or compound), to its total input, normally expressed as a percentage. ing Period – The period in which a company would normally report its financial results to the market. For example in the USA this is quarterly, while in many other financial markets the requirement is bi-annually. ing Standards – The financial ing standards required by the relevant regulatory authorities including Stock Exchanges. Accuracy – A measurement is accurate if it, or the average of a number of measurements, is close to the true value. In metallurgical operations this true value is unknown. In addition, there is often misunderstanding between the accuracy and precision, which is the measure of the spread of a number of measurements around their mean value. For these reasons it has been decided to adopt the following definition for accuracy, which incorporates the concept of precision, and it is based on that given in ISO 3534-1:1993. A measurement that is accurate is one that is free of bias and it has a dispersion (standard deviation) that is lower than a defined dispersion or indeed a probability density of a particular nature. The level of dispersion or the nature of the probability density is defined with the purpose of separating measurements that are entirely fit for a particular purpose or use from those that are not. Acid Mine (Rock) Drainage – The low pH effluent which seeps from waste rock, ore dumps or stockpiles of materials which have an insufficient capacity to neutralise the acidic products, containing the dissolution products of minerals. Actual Recovery – The sum of all useful or desired outputs from a process, divided by the sum of all inputs to that process, expressed as a percentage. Analyte – The component of a material whose concentration is determined by chemical analysis. Auditability – A -oriented quality requirement specifying the degree to which an application shall keep sufficient records to a financial or technical audit. Audit Trails – In metal ing, an audit trail is a sequence of paperwork/data entries that validates or invalidates metal ing entries. Many metal ing entries are based on those made to computers and the term could also be used for an electronic or paper log used to track computer activity. Barren Solution (Tails Solution) – The discard solution from a metal recovery process. Battery Limits – The boundaries of a metal ing system, across which all inputs to and outputs from that system are measured and balanced.

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Best Practical Means – The most precise method of performing a measurement taking into the risk, degree of importance, cost and practicality for the stream and variable being measured. Best Practices – The most accurate method of performing a measurement with the information and equipment available at that time. Bias – Bias is the difference between the mean result of one or more measurements and the true value of the quantity being measured. It also can be seen as a consistent difference between two, or more, measuring systems. (e.g. sender’s and receiver’s weights, the analysis of the same sample by two laboratories etc.) Bubble Method – A method whereby the flow of material through a process plant is interrupted to allow for equipment to be emptied in series to facilitate stock-takes (normally applied to high value materials in the latter stages of processing). Bulk Density – The density of a granular material normally stored in a bin, other container or stockpile, defined as the mass of the material divided by the volume occupied by the material (including the space between the particles). Calibration Test (weighers) – A test, using known weights and forces to load a scale, in order to determine the performance and operating tolerance compared to standard values. Certification (Test) – A calibration of a scale or flow meter in of accepted standards defined by the relevant government authority to permit its use for trade purposes and custody transfer. Certified Weight – A weight, which has been certified by the relevant national body, or an agent authorised by that body, to have a mass within a defined tolerance, to be used for certifying weighing instruments. Check in-Check out System – The system whereby all streams into and out of the Process or Plant, for which the balance is being performed, are measured, sampled and analysed. Closure Errors – The difference between the inputs and outputs for a complete metal balance (that is Check In-Check Out) over a plant or process, including inventory and stock changes, over a prescribed period, and zero. Coefficient of Variation – The quotient of the standard deviation and the mean value of a measurement, expressed as a percentage. Competent Person – A person with suitable qualifications and experience to make recommendations for the design and to undertake reviews of the performance of the Metal ing system and Balance. Coriolis mass flow meter – A device consisting of a flow sensor and a transmitter which primarily measures the mass flow by means of the interaction between a flowing fluid and the oscillation of a tube or tubes; it may also provide measurements of the density and the process temperature of the fluid. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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Corporate Governance – Is concerned with holding the balance between economic and social goals and between individual and communal goals. The corporate governance framework exists to encourage the efficient use of resources and equally to require ability for the stewardship of those resources. The aim is to align as nearly as possible the interests of individuals, corporations and society. Custody Transfer – The transfer of risk and/or ownership of a material from one party to a second party. This includes ore from a mine to a processing plant, intermediate products such as concentrate and matte from one plant to another, both in-house and inter-company and waste products for sale or safe disposal, as well as final products. Data – In a metal balancing context it is almost always a numerical measurement. Differential Pressure – The difference between the static pressures measured at the wall pressure tappings, one of which is on the upstream side and the other on the downstream side (or in the throat for a Venturi tube) of a primary device inserted in a straight pipe through which flow occurs, when any difference in height between the upstream and downstream tappings has been taken into . Division – The process of decreasing sample mass (without modification of the particle size of the constituent pieces) whereby one or more representative parts of the sample are retained. (AS 4433.2 – 1997) Draught Survey – A mass measurement technique that is based on converting a difference between a vessel’s displacement under different loads into a mass on the basis of its draught tables while taking into the density and temperature of the water and ballast, and changes in ballast and supplies Dump Leaching – Normally refers to a process whereby metals are extracted by "heaping" uncrushed/broken Run of Mine ore on sloping impermeable pads (with suitable low cost drainage of the leach solutions) and spraying the heaps with a lixiviant, which dissolves the metal content. Sometimes the process is carried out utilising low-grade ore which was previously stockpiled, as treatment through the normal process route was uneconomic. Often the lixiviant used is the barren solution from other primary leach operations. The metal-laden solution is collected for metal recovery. Electromagnetic flow meter – A flow meter, which creates a magnetic field perpendicular to the flow, thereby enabling the flow rate to be deduced from the induced electromotive force produced by the motion of a conducting fluid in the magnetic field. Electromechanical Belt Scale/Weigher – A mass measurement device whereby a loaded belt is continuously weighed by mechanical means (load cells, levers) and the measurement converted into an electronic signal that can be integrated (totalised) and recorded. Evaluation – The process by which the data required for Metal ing is acquired.

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Exception Report – A report generated, in of this Code, whereby each noncompliance with the requirements of this Code is motivated and approved. Gantry scale – A scale where the load measuring elements are attached to a gantry crane normally used for loading or offloading materials in bulk. Heap Leaching – Normally refers to the leaching of ores that have been crushed (to an economic size) prior to placement on an impermeable pad on which suitable drainage lines have been placed for the collection of lixiviant that has been percolated through the crushed and prepared ore. The crush size selected may vary considerably but typically it ranges between 12 and 50 mm. Heterogeneity – Is a measure of the dispersion of minerals/metals of interest within a quantity of material, in of mass or time. Home and Away System (Double Sampling and Analysis System) – A system where batches/lots are weighed and sampled by both the sender and receiver. Data from the analysis of the samples can be statistically analysed to establish the presence of biases in the sampling and/or analytical processes. In the “Half Home and Away System” samples from even numbered batches of material are analysed by the sampling sites laboratory (i.e. the sender’s sample at the sender’s laboratory and receiver’s sample at the receiver’s laboratory) and odd numbered batches are analysed at the other laboratory (i.e. the sender’s sample at the receiver’s laboratory and receiver’s sample at the sender’s laboratory). Statistical analysis of the data can establish the level of bias in either the sampling or the analytical procedures. Hopper Scale – A scale designed for the weighing of granular materials in bulk, whose load-receiving element is a self-cleaning hopper with an outlet gate. Illegitimate Errors (Spurious) – The error which invalidates a measurement. They generally have a single cause such as the incorrect reading of one or more significant digits or the malfunction of instruments. In-motion weighing – The weighing of objects that are in-motion. Usually applied to rail or road trucks. In-process Inventory – The quantity of material contained in a process plant through the various stages from the initial feed to the plant to the production of product(s) from the plant. It does not include stocks of feed material or products which are defined as Plant Stocks. Inter-leaved Sampling – To carry out interleaved sampling, a sampling system must be capable of taking increments at twice the usual rate. Odd-numbered increments are collected into one sample and the even-numbered ones are collected into a second sample over the sampling period of interest. Both samples are, at the end of the sampling period, nominally identical. If they are both separately prepared and analysed in the same way, the difference between the results is an indication of the precision of sampling. A substantial number of results from interleaved pairs of samples can be accumulated over a period of time to arrive at a good estimate of the average sampling precision. It is also possible to draw certain conclusions regarding the distribution of the assay uncertainties. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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International Unit of Mass – This is defined as the kilogram. The definitive standard weight is held at the International Bureau of Weights and Measures in , which is also responsible for certifying National standards such as the National Prototype Kilogram. LIMS – Acronym for Laboratory Information Management System, a computer system for gathering and analysing laboratory information. These systems are used for:

•

Automatic data collection. The LIMS may be interfaced directly to the laboratory instruments, offering rapid data collection, while eliminating the risk of transcription errors.

•

Provision of electronic records of laboratory results. Electronic records offer many benefits, such as the ability to link data together, search, retrieve and report rapidly and backup and disseminate data easily through an operation.

•

Provision of a structured repository for laboratory data

Mass Balancing – The determination through measurement, analysis and computation of the magnitude of each component of interest across an entire plant (Primary ing), or across each section of a process flow sheet (Secondary ing). Materiality – It is what affects business decisions at the top level of management. It is not a formal defined value but it should be determined by senior management for each company and/or operation. For metal ing, a measurement for a process flow or in-process inventory may be regarded as material if:

•

the metal content is significant in value and/or

•

the content is regarded as important with respect to the company’s or operation’s risk profile, public perceptions or reputation and/or

•

it will reflect on the balance sheet and financial statements

Material-run (bulk) Test – A method of calibrating a belt scale whereby a known weight of the material (either pre or post weighed on a test scale) being weighed is ed on the conveyor through the mass detection system. Measurand – The specific quantity subject to measurement. Mechanically Correct Sampling – Correct sampling of a particulate material from a moving stream is achieved when the expected number fraction of the particles in the sample is equal to the average number fraction of the particles in the process stream during the time that it takes to extract the sample increment. To achieve this objective, the sampler must be correctly designed and operated. A sampler that is mechanically correct is unbiased. Where possible, the correctness of the sampler and its freedom from a specified level of bias should be confirmed by carrying out bias tests by the stopped belt method or other suitable reference method of sampling.

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Metadata – The term used to describe data about data. It describes who collects the data, what the data contains, where (and how) the data is stored, when (and how often), why the data is collected and how it is processed. Metal ing – The system whereby selected process data (pertaining to metals of economic interest) is collected from various sources including mass measurement and analysis and transformed into a coherent report format that is delivered in a timely fashion in order to meet specified reporting requirements. Metallurgical (Metal) balance – The determination through measurement, analysis and computation of the magnitude of each component of interest across an entire process (Primary ing) or across each section of a process flow sheet (Secondary ing). (See Mass Balancing). A metallurgical balance, which is suitable for reporting of Metal ing, should be supplemented by defined uncertainties associated with each determination. In of this Code, a metallurgical (metals) balance encomes metals, minerals or commodities of interest. Metrology – The science of Weights and Measures. Mine Call Factor – The ratio, expressed as a percentage, which the specific product ed for in the product recovered from a plant, plus that in tailings/residues, bears to the corresponding product called for by the mine’s measuring and evaluation methods. It is also the product of the Shaft and Plant Call Factors. Momentum Mass Flow Meter (Impact Mass Flow Meter) – A device that detects mass flow rate by measuring the momentum of a stream of material falling a definite distance under gravity. National Prototype Kilogram – The standard certified weight which is held by individual countries and which has been certified by the International Bureau of Weights and Measures. This standard weight is used to certify, by the national Metrology organisation, the test weights used to certify all weighing equipment. Nuclear Belt Scale/Weigher – A mass measurement device that continuously integrates and records as a cumulative mass the load on a belt while it es the nuclear source by measuring the absorption of the gamma rays by the material on the belt. Nuclear Gauge – An instrument that emits a source of neutrons or radiation (usually gamma rays) which through the material in a conduit, on a belt, or in a stockpile or bin, and has a receptor that collects the radiation on the other side, thus enabling a measurement of the radiation adsorption and hence the moisture content or density of the material. Penetrometer – An instrument that measures in-situ density by measuring the backpressure resulting from the penetration of a cone into the material being measured. Usually applied to soils but is also utilised for stockpiles of material such as coal of a relatively fine uniform size.

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Plant Call Factor – The mass of a species recovered to product and to tailings of the plant divided by the mass of species fed to the plant, expressed as a percentage. Plant Historian – A computer system for accurate operational data archiving and its rapid recovery. The data historian accepts data input only, it does not recognise structures and procedures, and it is strictly concerned with accurate, efficient data storage and recovery if required. Data historians perform three key functions:

•

Data compression

•

Rapid data recovery

•

Data save-set facility

Plant Inventory – The inventory can be stockpiles of feed, intermediates or products (plant stocks) as well as transient and long-term in-process inventory. Plant Stocks – Any material received by a plant, from a previous processing operation, but which has not yet entered a process stream, or any material produced by that plant to dispatch standard quality, but which has not yet been dispatched. Platform Scale – A compound lever scale whose load receiving element is a platform that is ed by four or (rarely) more main bearings. Precision – Precise measurements have a dispersion about their mean value which is lower than a defined dispersion or indeed a probability density of a particular nature. The level of dispersion or the nature of the probability density is defined with the purpose of separating measurements that are entirely fit for a particular purpose or use and those that are not. A precise measurement may not be accurate; its mean may differ from the true value of the measured quantity by an arbitrary amount. Standards often quantify the term precision as a value corresponding to the magnitude of a 95% confidence interval around a result. The interval is ± ts, where t is the two-sided Student-t value at 95% confidence and s is the estimate of the standard deviation. Pregnant Solution – A solution containing dissolved metals (e.g. from a leaching operation), which is normally the feed to further processing to extract the product metals. Primary ing – The metal balance across an entire plant as determined by Check in-Check out. Random Errors – The component of the error of measurement, which in the course of a number of measurements of the same measurand, varies in an unpredictable way. Random Stratified Sampling – The taking of increments whereby the material being sampled is divided into strata, one increment being taken at random within each stratum. (AS 4433.1 – 1997). For example, if the sampling interval of a cross stream cutter is based on time, say every 5 minutes, the cutter is not triggered at 5, 10, 15 etc minutes but rather at a random time within each 5 minute interval.

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Reconciliation – A metallurgical balance which relates production of saleable and reject or waste materials from a process back to its source as ore or other feed material. It should be provided with defined and stated errors as for any other metallurgical balance. Recoverable Metal – The proportion of a metal in a feed material that has been determined to be recoverable, by laboratory and/or pilot plant tests, by the chosen process, with the remaining metal being defined as Non Recoverable. Recovery – Of a particular component is defined as the proportion of that component in the feed source, which is estimated to have reported to each process stream but most commonly to the saleable product. Hence, recovery is a measure of performance efficiency and it is always a key performance indicator. Reduction – The process of decreasing the dimension of pieces constituting the sample, without modification of mass of the sample or of its composition (AS 4433.2 – 1997). Risk – The chance of an event occurring that will have an impact on objectives. A risk may be quantifiable in of the likelihood of loss, less than expected returns or an undesirable outcome. (The VALMIN Code, 2005 Edition) Risk Assessment – A formal process for the identification and quantification of the risks associated with all the processes involved in metal ing. For example, a decrease in accuracy of measurement may lead to the true loss of metal exceeding a threshold value; a risk assessment will quantify the probability that the threshold is exceeded. Run of Mine (ROM) Ore – Ore produced from a mining operation, which has not been subject to any beneficiation. Sample Safety Line – Is usually constructed in a nomograph and relates the minimum sample mass required at a particular particle size to achieve a specified level of variance. Gy introduced the concept of a 'sampling safety line', which is based on the relationship between the introduced variance and the particle top size. If the sampling constant is taken to be dependent only on particle top size and the sampling constant is known at one particular state of comminution of the material, a 'sampling safety line' can be drawn for the particular analyte to which the sampling constant applies. Sampling Constant – The sampling constant for a material describes its heterogeneity with respect to a particular analyte. The sampling constant is usually proportional to the cube of the 95% ing size of the particles in the sample, but this is not always true. Important exceptions to this rule occur when sampling materials that are low-grade overall but in which the valuable component occurs as relatively large blebs or nuggets of high-grade material. Diamond ores, alluvial gold and PGM ore containing 'metallics', where the valuable component is present at g/tonne levels, are the most notable exceptions. Low-grade materials with small inclusions of high-grade material can have very large sampling constants. The sampling constant has a unit of mass, usually expressed in grams. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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SCADA – Acronym for Supervisory Control and Data Acquisition, a computer system for gathering and analysing real time plant data. SCADA Systems are used in industry to monitor and control plant status and provide logging facilities. As the name indicates, it is not a full control system, but rather focuses on the supervisory level. As such, it is a purely software package that is positioned on top of hardware to which it is interfaced, in general via Programmable Logic Controllers (PLCs), or other commercial hardware modules. Secondary ing – Conducting mass balances over smaller sections of the total plant circuit, to identify where lock-ups are occurring, time lags are involved or where any measurement problems exist. Shaft (or Mine Section) Call Factor – The mass of a species delivered to a shaft compared to the mass of that species calculated from sampling and surveying procedures, expressed as a percentage. Sonic Drilling – Sonic drilling involves drilling with a drill stem that is excited at a relatively high frequency, resulting in a standing wave being transmitted down the drill stem and back upwards. This vibration substantially reduces the friction between the stem and the material being drilled. It is claimed that core recovery is better for sonic drilling than conventional coring methods. Specific Gravity – The specific gravity of a substance is the ratio of the density of the substance at a specified temperature to the density of a reference substance at the same or a specified different temperature (usually the reference substance is water at a temperature of 4 ºC). Specific gravity is a dimensionless quantity and is also referred to as 'relative density'. Strata – Approximately equal parts of a lot or sub-lot based on intervals of time, mass or space (AS 4433-1:1997). Static Scales – A mass measurement device that converts a static load on a weighbridge or on a platform, inside a hopper or suspended from a gantry scale into a mass. Systematic Errors – The component of the error of measurement which, in the course of a number of measurements of the same measurand remains constant or varies in a predictable way. Three-Product Formula – Used in concentrators which produce more than one concentrate product (e.g. lead-zinc concentrators), to obtain a metallurgical balance for process monitoring purposes, where the component analyses of the feed and each product stream are known, as well as the mass flow of one of the streams either entering or leaving the plant. Tolerance – The limit of error or variance established by authority or usage or a value fixing the limit of allowable error or departure from true performance or value. (NIST Handbook) Traceability – The property of the result of a measurement or the value of a standard whereby it can be related to stated references, usually national or AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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international standards, through an unbroken chain of comparisons, all having stated uncertainties. Transparency – Is achieved when the method of measurement, analysis and calculation of a quantity is fully documented. Triple bottom line reporting – The triple bottom line (TBL) focuses corporations not just on the economic value they add, but also on the environmental and social value they add - and destroy. At its narrowest, the term 'triple bottom line' is used as a framework for measuring and reporting corporate performance against economic, social and environmental parameters. At its broadest, the term is used to capture the whole set of values, issues and processes that companies must address in order to minimize any harm resulting from their activities and to create economic, social and environmental value. This involves being clear about the company's purpose and taking into consideration the needs of all the company's stakeholders and shareholders, customers, employees, business partners, governments, local communities and the public. Twin-Stream (parallel analysis) – Is a system where a sample is analysed, preferably, by different analysts at different times using separately calibrated instrumentation. Alternatively, if the availability of different analysts is limited, the same analyst can repeat the analysis at different times. Two-product formula – Used in concentrators with minimal accumulation of inventory to calculate plant recovery, where Plant Recovery = (Cc/Ff) x 100% = 100c(f – t)/f(c – t) And C and F are respectively Concentrate and Feed tonnages And c, f, and t are respectively Concentrate, Feed and Tails grades. Ultrasonic Transit Time Flow Meters – A flow meter utilising the principle that the propagation velocity of sound waves within a fluid is directly influenced by the velocity of that fluid. Umpire Laboratory – A laboratory, chosen because of its expertise, to which samples with disputed results can be sent for arbitration. Uned Loss or Gain (Metal ing Variance) – Any discrepancy between actual recovery and the ability over an ing period. Uncertainty – A parameter, associated with the result of a measurement that characterises the dispersion of the values that could reasonably be attributed to the measurand. Weighbridges – Weigh scales designed to weigh road trucks or rail wagons where the lever system is usually below ground in a pit so that the vehicle or rail wagon can over and stop on the scale. In a large capacity scale it also refers to the AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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structural frame carried by the main bearing which s the load-receiving element. Yield – The quantity of coal (of a specified quality) obtained from Run of Mine Material expressed as a percentage. There are two subsets: 11 Theoretical Yield: obtained in laboratory from washability curves. higher than practical yield.

It is

12 Practical Yield: obtained on a plant after losses due to process inefficiency.

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AMIRA P754 Guidelines for Metal Balancing and Reconciliation – Release 3 February 2007 Page

1

INTRODUCTION .................................................................................1

1.1

SAFETY AND HEALTH

2

1.2

PRINCIPLES OF METAL ING

2

1.3

STANDARDS

4

1.4

TRACEABILITY

4

1.5

ACCURACY IN METAL ING

4

1.6

PRECISION IN METAL ING

4

1.7

ERROR TYPES

6

1.7.1

Random Errors

6

1.7.2

Systematic Errors

6

1.7.3

Illegitimate (Spurious) Errors

8

1.8

METAL ING RISK

8

1.8.1

Introduction

8

1.8.2

Misrepresenting the Performance of the Operation

9

1.8.3

Real Losses of an Unknown Nature

10

1.9

TRAINING

10

1.9.1

Plant and Laboratory Operators

10

1.9.2

Plant and Laboratory Management

11

1.9.3

Operations Management

11

2

DATA MANAGEMENT...................................................................... 12

2.1

INTRODUCTION

12

2.2

DATA ACQUISITION AND ENTRY

12

2.3

DATA DOCUMENTATION

13

2.4

DATA PROCESSING

13

2.5

QUALITY CONTROL (QC) AND ASSURANCE (QA)

14

2.6

DATA FORMATS

14

2.7

DATA STORAGE AND BACK-UP

14

3

MASS MEASUREMENT ................................................................... 15

3.1

INTRODUCTION

15

3.2

ACCURACY AND PRECISION OF MASS MEASUREMENT

17

3.2.1

Scales/Weighing Equipment

18

3.2.2

Flow Meters

19

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3.3

METHODS OF MEASURING MASS

20

3.4

MASS FLOW MEASUREMENT

21

3.4.1

Mass Flow in Rail/Road Trucks

21

3.4.2

Mass Flow of Feed Materials, Products and Intermediates

23

3.4.3

Mass Flow on Conveyors

24

3.4.4

Mass Flow in Conduits

28

3.4.5

Mass Flow in Open Channels

32

3.4.6

Mass Flow for Bulk Commodities

32

3.4.7

Mass in Storage Areas

33

3.4.8

Mass in Stopes/ Ore Blocks

33

3.4.9

Mass in Stockpiles

34

3.4.10

Mass in Tanks and Bins

34

3.5

MASS IN PROCESS INVENTORY

35

3.5.1

Transient

35

3.6

LONG TERM

35

3.7

DENSITY AND MOISTURE MEASUREMENT

36

3.7.1

Bulk and Laboratory Tests

37

3.7.2

Nuclear Gauges

37

3.7.3

Microwave Gauges

38

3.7.4

Penetrometers

38

4

SAMPLING........................................................................................ 39

4.1

INTRODUCTION

39

4.2

SAMPLING ERRORS

40

4.3

STATISTICAL BASIS OF SAMPLING THEORY

43

4.3.1

Intrinsic heterogeneity of a particulate material

44

4.4

ACCURACY OF THE SAMPLING SYSTEM

54

4.4.1

Reference Sampling Method

56

4.4.2

Slurry Sampling

57

4.4.3

Stock-pile or Bin Sampling

57

4.4.4

Truck, Wagon, Skip and Drum Sampling

57

4.4.5

Solution Sampling

58

4.4.6

Final Metals and alloys

58

4.4.7

Scraps and residues

58

4.5

PRECISION OF THE SAMPLING SYSTEM

59

4.6

4.6 MULTI-STAGE SAMPLING SYSTEMS

61

4.7

SAMPLE STORAGE PRIOR TO DELIVERY TO A LABORATORY

62

4.8

SAMPLE LABELLING

62

5

SAMPLE PREPARATION AND ANALYSIS ..................................... 63

5.1

INTRODUCTION

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5.2

SAMPLE PREPARATION

63

5.2.1

Moisture Determination

63

5.2.2

Sample Reduction

63

5.2.3

Sample Mixing

64

5.2.4

Sample Division

64

5.2.5

Sample Storage After Sample Preparation

65

5.3

ANALYSIS

65

5.3.1

Laboratory Accreditation

65

5.3.2

Twin Stream (independent parallel) Analysis

65

5.3.3

Reference Materials

65

5.3.4

Instrumental Calibration (I, AA, UV/VIS, XRF [solutions])

66

5.3.5

Instrumental Calibrations (XRF [Pressed Powder and Fused Bead])

66

5.3.6

Instrumental Calibrations (Other)

66

5.3.7

On-Line Analysers

66

5.3.8

Fire assay

66

5.3.9

Mineralogical Analysis

67

5.3.10

QC Procedures

67

5.3.11

QA Procedures

67

5.3.12

Umpire Laboratory

67

6

STOCK-TAKES................................................................................. 69

7

METAL BALANCING........................................................................ 71

7.1

INTRODUCTION

71

7.2

THE METAL ING PROCESS

72

7.3

METAL BALANCING

75

7.4

RECONCILIATION OF METAL BALANCES

79

7.4.1

Detecting Random Error (or Illegitimate/Spurious Errors)

81

7.4.2

Bias

81

7.4.3

Estimating Flow Rate Accuracy

81

7.5

METAL BALANCING APPLICATIONS

82

7.5.1

Concentrators with minimal accumulation

82

7.5.2

Concentrators with Intermediate Accumulation

84

7.5.3

Coal Treatment Plants

84

7.5.4

Pyrometallurgical Plants

84

7.5.5

Hydrometallurgical Plants

89

7.5.6

Heap Leaching

90

7.5.7

Bench Scale, Pilot and Demonstration Plants

94

7.6

MINE TO PRODUCT BALANCING AND RECONCILIATION

98

7.7

MASS BALANCING FOR ENVIRONMENT AND HEALTH MONITORING

98

7.7.1

Gases and Dusts

99

7.7.2

Liquid Effluents

99

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8

REPORTING ................................................................................... 100

9

SELECTION AND USE OF A COMPETENT PERSON .................. 102

10

DESIGN OF METAL ING SYSTEMS ............................ 104

11

METAL ING AUDITING RULES.................................... 107

11.1

INTRODUCTION

107

11.2

AUDIT TEAM AND ITS RESPONSIBILITIES

107

11.3

METAL ING REVIEW

108

11.4

PHYSICAL AUDITS

108

GLOSSARY.................................................................................................... 109 LIST OF ACRONYMS AND ABBREVIATIONS ............................................. 128 APPENDICES................................................................................................. 129 APPENDIX A – ACKNOWLEDGEMENTS..................................................... 129 APPENDIX B – REFERENCES...................................................................... 131 APPENDIX C – STANDARDS........................................................................ 139 APPENDIX D – CHECK-LISTS ...................................................................... 150 APPENDIX E – MASS BALANCING TECHNIQUES ..................................... 159 APPENDIX F – DETERMINATION OF THE SAMPLING CONSTANT.......... 171 APPENDIX G – ERROR AND VARIANCE PROPAGATION IN TOLL SMELTING OPERATIONS TREATING MULTIPLE CONCENTRATE STOCKPILES.......................................... 174 APPENDIX H – USEFUL WEB SITES ........................................................... 192

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1

INTRODUCTION

The AMIRA P754 project has the primary objective of improving the auditability and the transparency of Metal ing from mine to product, thus enhancing corporate governance. The project is ed by five sponsor companies in South Africa and Australia and it involves two academic supervisors and seven students. Project areas have broad coverage from precious metals, to commodities, and mine to product. This document is intended to serve as a set of Guidelines for the Code on Metal ing, which will in the future be controlled by suitable professional organisations closely associated with the industry, and gain industry acceptance worldwide. It should also be regarded as a living document which will be revised through contributions from sponsor companies, the related research program and other initiatives. In this Guidelines document, unless otherwise specifically stated, references to the “Code” include both the Code and the Guidelines. A number of sponsor companies and others involved in, or connected to, the metals production industry have contributed to the contents of this document through the provision of internal standard procedures and/or specifications, in assisting with literature searches and by providing advice and information. A list of these companies and individuals is included in the Appendix A, and their contributions are gratefully acknowledged. The aim of this document is to provide standard generic procedures and guidelines for obtaining credible figures of quantities of materials processed and produced and further, to be recognised and accepted as the Industry guidelines for best practice. In cases where companies or operations are unable to comply with the best practices set out in the Code and these Guidelines, the reasons for this noncompliance must be documented in the form of an Exception Report. This must clearly set out the reasons for the non-compliance, the steps taken to mitigate the effects of the non-compliance, the resultant impact on the overall integrity of the metal ing system and plans to ensure compliance in the future. This Exception Report must form part of the documentation of the metal ing system and, as such, must be approved by the Competent Person responsible for the design of the metal ing system, and by the Audit Committee of the company concerned. These Guidelines should facilitate full disclosure of operational performance, including losses in gas emissions, (of S as SO2, for example) or other effluents from operations, in line with requirements for triple bottom line reporting in financial statements There are no differences in principle between base and precious metals and commodities, but there are differences in the way the Code/Guidelines would be applied. These Guidelines set out principles for obtaining credible figures, such as the methods to be used and their accuracy, recommended methods for mass measurement, sampling and analysis and the applicable International Standards to achieve this, as well as the basis for the design and management of a Metal ing system. More details are included in the Appendices. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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The Code and Guidelines are aimed at working with quantities whereas valuation of these quantities is a separate exercise. The Guidelines together with the appropriate use of statistics to define confidence levels provide a framework for metal ing. Due to the random variations that are inherent in all measurement processes, the values derived in metal ing will not be exact but will have a degree of uncertainty. However, in correctly designed and operated systems the level of uncertainty is measurable and should be quoted in the ing reports. If the measured precision value is outside of the expected range, and the range will vary depending on the materiality of the measurement concerned, its cause should be identified and rectified. Because of the naturally wider variations in dynamic metallurgical processes, it is usual to use shift data for plant control purposes. The metal ing system combines shift data into an average, “to date”, number that is normally reconciled and reported on a monthly basis. This data is then further combined on a quarterly or six monthly basis to suit the company’s financial reporting requirements.

1.1

SAFETY AND HEALTH

It is expected that internal company procedures, in compliance with local laws and regulations, will provide fully for the safety and health of all staff, which must always be of primary importance in all of the activities involved in Metal ing. For example, in the design of metallurgical plants, the positioning of weighers and samplers should be such that access to them for routine operation, inspection and cleaning can be done in a completely safe manner. Staff must be trained in the safety and health aspects of their activities and supplied with the necessary Personal Protective Equipment (PPE). The possibility of potential exposure to hazardous dusts, gases and chemicals, as well as other hazards, must be fully understood and adequate precautions implemented.

1.2

PRINCIPLES OF METAL ING

The basic principles of metal ing are summarised below: 1

The metal ing system must be based on accurate and precise measurements of mass and metal content. It must be based on a full Check in-Check out system using the Best Practices, as defined in this Code, to produce an on-going metal/commodity balance for the operation. The system must be integrated with management information systems, providing a one-way transfer of information to these systems as required.

2

The system must be consistent and transparent and the source of all input data to the system must be clear and understood by all s of the system. The design and specification of the system must incorporate the outcomes of a risk assessment of all aspects of the metal ing process.

3

The ing procedures must be well documented and friendly for easy application by plant personnel, to avoid the system becoming dependent on one person. The procedures must incorporate clear

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controls and audit trails. Calculation procedures must comply with the requirements set out in this Code; they must be consistent at all times and must include clear rules for handling the data. 4

The system must be subject to regular internal and external audits and reviews as specified in the relevant sections of the Code, so as to ensure compliance with all aspects of the defined procedures. These reviews must include assessments of the associated risks and recommendations for their mitigation, when the agreed risk is exceeded.

5

ing results must be made available timeously, to meet operational reporting needs, including the provision of information for other management information systems, and to facilitate corrective action or investigation. A detailed report must be issued on each investigation, together with management’s response to rectify the problem. When completed, the plan and resulting action must be signed-off by the Competent Person.

6

Where provisional data has to be used to meet reporting deadlines, such as at month ends when analytical turn-around times could prevent the prompt issuing of the monthly report, clear procedures and levels of authorisation for the subsequent replacement of the provisional data with actual data must be defined. Where rogue data is detected, such as incorrect data transfer or identified malfunction of equipment, then the procedures to be followed together with the levels of authorisation must be in place.

7

The system must generate sufficient data to allow for data verification, handling of metal/commodity transfers, reconciliation of metal/commodity balances, and measurement of accuracies and error detection, which should not show any consistent bias. Measurement and computational procedures must be free of a defined critical level of bias.

8

Target accuracies for the mass measurements and the sampling and analyses must be identified for each input and output stream used for ing purposes. The actual accuracies for metal recoveries, based on the actual accuracies, as determined by statistical analysis of the raw data, achieved over a company’s reporting period must be stated in the report to the Company’s Audit Committee. Should these show a bias which the Company considers material to its results, the fact must be reported to shareholders.

9

In-process inventory figures must be verified by physical stock-takes at prescribed intervals, at least annually, and procedures and authority levels for stock adjustments and the treatment of uned losses or gains must be clearly defined.

10 The metal ing system must ensure that every effort is made to identify as rapidly as possible any bias which may occur and to eliminate or reduce to an acceptable level the source of bias from all measurement, sampling and analytical procedures once the source is identified.

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1.3

STANDARDS

Procedures used to provide data for Metal ing must be undertaken to the requirements of the appropriate International or National Standard Institutions, which have developed these to cover a wide range of mining and metal products. Where these standards are not available, great care should be taken to maintain the accuracy of procedures.

1.4

TRACEABILITY

In the areas of mass measurement and analysis, the aim should be to establish traceability to international standard (SI) units.

1.5

ACCURACY IN METAL ING

All of the operations and equipment used to derive data for the reporting of Metal s, which include mass measurement, sampling, sample preparation and analysis, should be designed and operated to achieve an accurate result. As defined in this Code, accuracy is related both to random errors (Section 1.7.1), which are an integral part of the measurement process, and to systematic errors (Section 1.7.2). Bias occurs as a result of systematic errors which occur when the mean value of the random errors is not zero over an extended period which contains many measurements. Bias is difficult to detect but it can and should be eliminated (or at least minimised) because its effect accumulates in any cumulative measures of production or performance. Accuracy should not be confused with Certification. Certification is required to provide an agreed basis for commercial transactions and Custody Transfer. A certified measurement will contain random errors and it may also contain some degree of bias. However, for the purposes of the commercial transaction, the numerical result is considered to be an absolute value and it is used as the basis of a monetary transfer calculation. A metal ing system, which is performing well, will provide good agreement between the metal balance, calculated using many data inputs, and the commercial values used as the basis of commercial transactions. However, the values only need to be similar to within the precision of the Metal s.

1.6

PRECISION IN METAL ING

The variances of independent operations during a process are additive. In Metal ing this can be represented by the following equation: VTMA = VM + VS + VSP + VA

[Eqn 1.1]

where: VTMA

=

the Total Metal ing variance

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VM

=

the variance due to Mass Measurement and the determination of moisture

VS

=

the variance due to all of the Sampling Stages

VSP

=

the variance due to the Sample Preparation

VA

=

the variance due to the Analysis

VM can be further divided to show the variance due to the determination of the wet mass, including the components of the mass measurement process, and that due to the determination of the moisture of the material. In a multi-stage sampling process, VS should be expanded to show the variance of each stage. Where the processes are not independent, (i.e. they are related by a set of procedures or combined calculations), it is appropriate to use a more general relationship called the Propagation of Variance which takes covariance of quantities into . This relationship allows the variance of calculated values (such as Recovery) to be estimated in a statistically correct manner. Because variance is a squared term, (it is equal to the square of the standard deviation) the operation with the largest variance will have the major impact on the Total Metal ing variance. For this reason, it is important that the precision of each operation is measured and that it has an acceptable value. An operation in this context can mean any mass measurement or sampling and analytical procedure. The precision for mass measurement, sampling and assaying can be established by carrying out suitable experiments that are described in a number of International Standards. The operation with the highest relative variance, i.e. the poorest precision, can be identified and steps taken, if necessary, to reduce the value of the variance. For example, the number of increments at a sampling stage could be increased to reduce VS, or the number of replicates taken for the analysis could be increased to reduce VA. If it was decided that the operation with the highest variance could not be improved, for example, due to the cost involved, there would be no purpose in reducing the variance of any other operation to be significantly below this highest value. In typical metallurgical plants, where process streams are sampled and analysed on an 8-hour shift basis and about 90 data sets are produced per month, relative precision levels (one standard deviation) of 3% for each measurement are acceptable, because this equates to a monthly precision of 0.32%. However, where individual shipments/lots are being evaluated, such as in toll treatment, individual precision levels of < 0.5% will probably be required. For operations generating a final metal product, commercial transfer precision will usually be a small fraction of the above targets, but the required precision for coarse mill feed (f80 of 100 mm or larger) may not be achievable without a substantial investment in sampling and sample reduction.

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From the measurement of VTMA, it is possible to state the uncertainty in the Metal s, because: VTMA = σ2TMA

[Eqn 1.2]

where: σ TMA = the Standard Deviation of the Metal s and, by convention, it is usual to quote the uncertainty at the 95% confidence level, which is equal to ± 2 × σ TMA. It should be noted that biased measurements in the material balance calculations would cause the final result to be biased. It is therefore very important that measurements are accurate as well as precise.

ERROR TYPES

1.7

Errors in measurement can be classified into three types:

1.7.1 Random Errors Random errors represent the variation in results obtained under the same or nearly identical conditions. They are the scatter of the observed data around the actual value and, as such, their average will tend to zero over time. They are caused by natural fluctuations in the measurement process and cannot, therefore, be eliminated but they can be minimised by careful operation. With mass measurement such factors as:

•

mechanical or electrical tolerances in the equipment

•

fluctuations in temperature

•

vibration and instrumental noise

will contribute to random error, and, because they are statistical in nature, they can be evaluated and quantified by standard statistical methods. Most random errors are assumed to be drawn from a normal distribution. If the measurement in question is the mean of repeated measurements, then this assumption will always be approximately correct, the approximation improving as the number of repeated measurements increases. However, if the probability distribution of random errors is highly asymmetric (i.e. there is a chance of individual measurements being much larger numerically than they will be smaller, or vice versa), then special techniques are required to ensure that the metal balance will provide the most likely balance, which will not usually be the mean balance.

1.7.2 Systematic Errors Systematic errors result in data that is consistently higher or lower than the true value, leading to a biased situation. Increasing the number of measurements will not reduce the average of this type of error to zero. However, errors may arise from more than one cause at the same time and these may be in opposite directions and they tend to cancel one another out. This makes the detection and quantification of AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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systematic errors difficult and they can only be identified and eliminated by careful calibration and analysis of data. Measurement error distributions can be truly random but they have an expected value or mean that is non-zero and still cause bias as noted in the previous paragraph. For example, in mass measurement, the most common form of bias is a small instrument offset. If this offset is small compared with the random error of the measurement, it is essentially impossible to detect from a single measurement. A large quantity of redundant measurements, which contribute to the balance, may assist in finding the bias or spurious error (Section 1.7.3). Given a situation in which it is known that the expected value of a quantity should be zero (e.g. the difference in assays of the same sample between two laboratories), it is possible to detect a small bias by simply summing the differences over time. A bias component in the difference will accumulate, while the random errors of zero mean will tend to cancel. A plot of this cumulative sum of the differences is known as a CUSUM graph. CUSUM graphs are useful tools for detecting and measuring the level of the systematic errors, especially if these are relatively small. CUSUMS, or ‘cumulative sum’ methods, are very valuable for tracking the statistical parameters of a measurand. They can be used to track the bias between two measured quantities or they can be used to track the variance of a measurement. In the past, the application of ‘cumulative sum’ methods implied that the differences between measurements were simply summed over time. If the cumulative sum value increased with time, it was interpreted as meaning that there was a real positive difference between the values. Such a simple basis for CUSUM methods does not, however, take advantage of recent advances in statistical development. There are far better methods for formulating CUSUM procedures. These are described in Hawkins and Olwell. They describe methods that can be ‘tuned’ to detect changes in the statistical parameters of measurements and they can also be tuned to provide the desired speed of response to changes with a given level of risk of ‘false alarms’. Even without bias, these differences are themselves subject to random error and they can be expected to be negative or positive for a few times in succession. This makes the recognition of a bias quite difficult. In some measurements there are known errors within a system that cannot be eliminated but they must be quantified and allowed for. These errors, which are inherent in the method being used, produce a bias from the true value. For practical reasons, these methods are utilised rather than an alternate method, which would give a result closer to the true value. Examples in metallurgical ing systems are the:

•

K-factor in electromechanical weighers

•

Correction factors in the lead collection fire assay of the PGMs

•

Silver correction in the fire assay of gold samples

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The assay factors are established by parallel analysis of samples using a superior, more accurate method. It is important that the magnitude of these factors must be established on at least a monthly basis by comparison to alternative measuring procedures. The use and value of these factors must be agreed and signed-off by the Competent Person. For metal ing, fire assay should be avoided and if used, it must be motivated by an Exception Report.

1.7.3 Illegitimate (Spurious) Errors Illegitimate errors are caused by such factors as incorrect readings by the operator, incorrect calculations or abnormal sources of disturbances. Sometimes these are obvious because the result is far from the expected value. They can be eliminated or reduced by careful supervision and monitoring. A common source of spurious errors is the accidental swapping of samples (or marked containers) during the collection or analysis program. Collecting mean and standard deviations for each measurement point provides a simple indication of the occurrence of errors. If any measurement is more than three standard deviations from the long term mean, it must receive further examination.

1.8

METAL ING RISK

1.8.1 Introduction Any production operation is exposed to financial risk associated with its metal ing process, to the extent that there is uncertainty in the evaluation of the quantities of metal delivered to that operation and produced by it. In addition, there is the risk to the reputation of a company and of negative market perceptions resulting from major metal ing discrepancies. One of the prime objectives in the design of a metal ing system is to enable an operation to quantify, manage, and minimise the level of risk to which it could become exposed through failures or shortcomings in its metal ing system. Each operation must conduct an initial risk assessment of its entire Metal ing system to establish and quantify the risks. The design of a Metal ing system must take these risks into consideration and set down procedures to be followed when ing discrepancies are identified. The use of statistical analysis and check measurements and analyses provides a database, which becomes an essential component of this risk management process. The other fundamental component of the risk management process is regular and frequent management reviews, incorporating risk assessment, of the entire metal ing system, ed by both internal audits and external audits by the Competent Person. The main areas of risk to which an operation could be exposed in its metal ing system are listed below.

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1.8.2 Misrepresenting the Performance of the Operation 1.8.2.1 Incorrect weighing, sampling and analytical procedures These procedures need to be drawn up in line with international standard procedures (e.g. ISO 9002) while analytical quality control procedures must be followed and corrective action taken as appropriate. Where continued laboratory biases are detected, independent (umpire) laboratories must be used for verification purposes.

1.8.2.2 Inaccurate weighing equipment Weighing equipment must be correctly installed and comply with international or local standards and it must be routinely calibrated according to standard procedures.

1.8.2.3 Incorrect recording of receipts or dispatches Receivers’ weights must be checked against dispatchers’ weights with a third scale used for arbitration purposes when necessary. Any anomalies must be highlighted by the record-keeping system and investigated immediately and all records must be retained for audit purposes for a pre-determined period (usually to comply with local regulations).

1.8.2.4 Inaccurate sampling equipment Sampling equipment must be designed, installed and operated to ensure mechanically correct sampling and the ‘Home and Away’ (Double sampling and analysis) statistical system must be used to identify analytical and/or sampling biases.

1.8.2.5 ing samples mishandled or misplaced Clear sample handling and preparation procedures must be in place to avoid the risk of sample contamination or loss, or of a sample being inadvertently substituted for another. Clear record keeping is essential to track all sample movements and it is required as part of the metal ing audit trail.

1.8.2.6 Incorrect estimation of stockpiles Procedures exist for stock estimation and this topic is discussed further under Section 3.4.9 of this document. Stockpile estimates must be based on zero measurements taken at regular intervals (at least annually), as prescribed in the design of the metal ing system concerned.

1.8.2.7 Arbitrary methods to calculate metal recoveries Standard calculation procedures must be defined in line with the principles of metal ing (Section 1.2), based on a full Check in-Check out system, and they must be adhered to at all times.

1.8.2.8 Use of illegitimate or biased data Procedures for reviewing data, in order to eliminate illegitimate or biased entries from the metal ing database, need to be established, with AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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clearly defined levels of authority for the approval of these changes. Any such changes must be ed by documented technical evidence showing the reason for the proposed change(s), signed off by a suitably qualified independent person, and submitted to the company’s Audit Committee for final approval.

1.8.2.9 Incorrect calculations The metal ing system must incorporate automatic checks, to calculated results and to identify areas of error or uncertainty before the results are reported.

1.8.3 Real Losses of an Unknown Nature Typically, these are related to:

•

Theft of material from the processing operation(s)

•

Theft of material while in transport

•

Unauthorised dispatch of material to alternative customers

•

Losses caused by wind or run off, particularly while in storage or being handled

•

Failure to identify and investigate ability problems or anomalies promptly

The design of a metal ing system must take these factors into consideration, including the operation’s security arrangements and defined procedures to be followed in the event of ing discrepancies being identified.

1.9

TRAINING

As stated in Section 1.1 on Safety and Health, it is expected that companies will have full training procedures, in line with National and International Standards, such as ISO 9000, 14001 and 17025. The training requirements of staff involved in Metal ing will obviously vary depending on the role they are playing. Appropriate sections of the Metal ing Guidelines must be available to staff so that they can understand their part in the total system and the requirements necessary to produce unbiased data to a known and acceptable precision.

1.9.1 Plant and Laboratory Operators Plant Operators must be aware of the importance of the metal ing system to their operation and of the design and operating criteria of correctly installed sampling and mass measurement systems, and especially of the need for cleanliness around this equipment. The care required in the storage, labelling and handling of samples before they are delivered to the laboratory must be clearly understood. Laboratory Operators must be trained in the analytical procedures and equipment used for the analysis of Metal ing samples. The importance of their contribution to the Metal ing process must be emphasised. Laboratory Operators should also understand how to use and interpret Shewhart and CUSUM graphs. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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1.9.2 Plant and Laboratory Management Plant Management must have a detailed knowledge of the design, installation and operational requirements of Metal ing systems. They should be able to plan and implement inter-leaved experiments to establish the precision of sampling and mass measurement equipment and bias tests to establish their accuracy. Laboratory Management must ensure that their analytical procedures are accurate and that they are adequately monitored by quality assurance and quality control procedures. There is a requirement for an in-depth understanding of statistical analysis techniques. It is likely that a 4-year tertiary qualification will be required for both Plant and Laboratory management.

1.9.3 Operations Management Operations Management, in particular the “Champion” of the Metal ing system, must ensure the upfront, well-designed inclusion of evaluation systems into processing plants and be aware of their ongoing maintenance and operational requirements. There must be an awareness of the quality assurance data required to confirm that the systems are operating to specification. Liaison must be established between the various internal disciplines involved in metal ing and the appointed Competent Person. There must be an understanding of the relevance of Metal ing, its relationship to good corporate governance and the triple bottom line.

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2

DATA MANAGEMENT

INTRODUCTION

2.1

The operation of a Metal ing System requires the accumulation and evaluation of process data (e.g. mass measurements and analyses) from various sources (e.g. SCADA, Plant Historians, LIMS systems, etc). The acquisition, processing and management of data require a substantial portion of the effort associated with Metal ing. Efficient and reliable data management adds considerable value to the Metal ing System. The goal of data management is to create files from data obtained from various plant sources in such a way as to prevent error during transcription and to enable analysis of the errors inherent in the raw data. Production (e.g. mass measurement) and laboratory data (e.g. analyses) must be integrated to a single interface to facilitate Metal ing. At the same time, it is essential that those responsible for entering plant operating data, and those entering analytical results into the system, operate independently of each other.

DATA ACQUISITION AND ENTRY

2.2

There are a number of ways of acquiring or entering information for use in a Metal ing System. Data may be acquired from various plant systems such as the SCADA, the Plant Historian or external systems such as a LIMS system. Manual entry of data to operator log sheets and transposition to spreadsheets is common practice on some plants. Spreadsheets do not allow for controlled data entry and they are easily corrupted. It is easy to inadvertently type over data, lose track of records, duplicate and improperly enter data in a spreadsheet. For these reasons spreadsheets must be avoided as a method of data entry. However, because of the possibility of rogue communication between on-plant instrumentation and the Plant Historian, manual back up is necessary. Many errors can occur when entering or acquiring data and these need to be guarded against. Common examples of errors are:

•

Transpositions

•

Copying errors

•

Routing errors (e.g. information incorrectly routed from a SCADA system)

•

Consistency errors

•

Range errors (e.g. a value outside the plausible range for a variable)

Methods to avoid such acquisition or input errors include:

•

Manual checks during data collection (e.g. checks for completeness)

•

Range and consistency checking of data

•

Double entry and validation of manually inputted data

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•

Data analysis for outliers

Many of these checks and balances can be incorporated in metal ing software systems.

DATA DOCUMENTATION

2.3

Metal ing systems must include sufficient documentation of the data (metadata) that is being utilised for the various calculations. The objectives of compiling data documentation for metal ing data are:

•

To ensure data transparency

•

To ensure that the data is consistently well described

•

To ensure that quality and limitations of the data are apparent

•

To ensure that sufficient information is available to assess the suitability of data obtained and to enable the accuracy to be determined

Measurement or Sample Collection Documentation of the Sampling / Measurement System must include:

•

A description of the equipment used to obtain the sample or measurement and the manner in which it is to be operated

•

Details of the calibration of the measurement device

•

A description of any limitations associated with the chosen sampling or measurement strategy

Sample Preparation and Analysis Documentation for Sample Preparation and Analysis must include:

•

How the sample is prepared

•

A description of the stages of sample preparation

•

A reference to the documentation of the analytical procedure

•

A description of how instrumental error corrections are applied, if applicable

•

A description of the expected precision, accuracy, repeatability, and reproducibility of the method and the limits of detection

•

Details of methods used to validate sample preparation methodologies

•

Comments on any limitations

DATA PROCESSING

2.4

Documentation for the Data Processing System must include:

•

A clear description of the different stages of how the data is processed, including algorithms, statistical analysis, etc.

•

A description of the algorithms and computer programmes used

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•

Comments on limitations

QUALITY CONTROL (QC) AND ASSURANCE (QA)

2.5

This system must:

2.6

•

include a description of QC and QA procedures

•

document missing data or errors

DATA FORMATS

All data formats must be suitable for inclusion in computer files and they must be well documented to be readily accessible to all authorised s, in order to avoid misunderstandings and to prevent the data from becoming unusable. To achieve this, the documentation must include details of the provenance of a number, including its units of measurement, as well as the method by which it was obtained.

2.7

DATA STORAGE AND BACK-UP

Data loss can occur due to natural disasters, theft, human error and computer error. It is therefore necessary to take adequate steps to protect/store Metal ing data once it has been collected. It is essential to back-up data and documentation files on a regular basis. Back-up systems must be thoroughly tested to ensure that files remain uncorrupted and that they can be restored. Procedures must be written up for personnel unfamiliar with back-up and restore methods.

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3

MASS MEASUREMENT

INTRODUCTION

3.1

The overriding objective of mass measurement for Metal ing purposes is to establish the mass of the particular material or component present at a specific time, or the mass flow of that component over a defined time period, to a defined accuracy suitable for mass balancing. For the purposes of these Guidelines, “mass” should be read as “mass or volume,” as often for practical purposes, volume is measured (e.g. stockpiles or fluid flow). In these cases the specific gravity/relative density of the slurry, and of the solids and/or liquids, and/or bulk density, must also be determined by appropriate methods in order to calculate the mass. Materials often contain moisture, the proportion of which must also be established in order to obtain the dry mass. Wherever possible the mass measurement must be:

•

repeatable to within the defined error criteria

•

reproducible to within a defined error criteria, using different methods or equipment

•

unbiased, or alternatively capable of being tested to establish bias over a period of time

•

traceable back to the accredited standards used for calibration and certification

In order to achieve the above the system must be:

•

carefully selected to suit the specific application

•

specified, manufactured, designed and installed to appropriate guidelines

•

installed in such a manner as to permit operation, maintenance and calibration to be carried out safely

•

operated and maintained in an appropriate manner

•

calibrated regularly according to recommended standards and guidelines

•

assessed by the collection and statistical analysis of the operating and calibration data and checks to determine the accuracy of the measurements

•

certified by the appropriate authorities or test facilities as required

The mass measurements necessary for metal ing purposes are normally required to measure the deportment of valuable components and/or for legal and environmental requirements. As such, they must be within appropriate limits of accuracy to satisfy shareholders and stakeholders. Thus, the equipment and the calibration techniques used must be capable of producing results, which are superior to those which may be used for management data or for plant control, where the consistency of the measurement is more important than the absolute value and thus some bias may be acceptable. The acceptable accuracy should also take of Best Practices, bearing in mind the cost/benefit relationship, the difficulty of AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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measuring and sampling the process stream and the importance of the measurement to the metal balance. Mass measurement can be classified into three areas:

•

Measurements necessary for custody transfer and Primary Metal ing

•

Measurements required for Secondary Metal ing, and management control including intra company transfers and plant area performance, which may also be used for cross checks on Primary ing. This also includes measurements necessary for environmental monitoring

•

Measurements required for plant control only.

The requirements for the accuracy of the measurement are the most stringent for Primary ing, requiring certified (assized) weighing equipment and the ability to establish the distribution of the random errors associated with the measurements. Accuracy requirements are less for Secondary ing, with those for plant control requiring the lowest level of accuracy. This Code is concerned with the requirements for the first measurement area only. Secondary ing measurements are used for checking and investigation purposes, in conjunction with other process data, where confirmation of Primary ing information is required. Normally, final saleable products are weighed on certified (assized) weighing equipment and sampled and analysed according to laid down procedures agreed between seller and purchaser, usually to accepted standards for custody transfer. The mass measurement of process streams for Primary Metal ing must be carried out to the same level of accuracy as those for custody transfer or commercial transactions. This means that certification, and the ability to establish the distribution of the random errors associated with the measurements, are requirements. Feed material to metallurgical plants is often ore, supplied from mining operations in large quantities, which has a wide and variable size range and moisture content. Mass measurement of this stream is required for Primary ing but, in practice, it is often weighed in trucks or hoppers, or on conveyor belts, to a lower level of accuracy than required for the final products. Achieving a high degree of accuracy for this type of material is more difficult and costly and it requires appropriate design, selection and installation as emphasised above. If it is felt that the cost and effort to achieve certification is not justifiable, then this must be motivated by way of an Exception Report. In all weigher and flow meter installations, it is essential that the selection, system design, installation, operation, maintenance and calibration follow the applicable standards and manufacturers’ recommendations, in order to achieve the accuracy and precision levels quoted in Section 3.2, and those which are required by International and National Standards and National Metrology Acts.

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ACCURACY AND PRECISION OF MASS MEASUREMENT

3.2

The various possible methods of measuring mass or mass flow are described in Section 3.3. In of mass measurement, accuracy may be defined in several different ways, such as maximum percentage error of reading, or maximum percentage error at full scale of the instrument or equipment, and it is critical to ensure that the definition of levels of accuracy in supplier’s documentation or the literature is comparable with those used in this Code and in the applicable Standards. The accuracy of mass measurement is affected by the:

•

material being measured (particle size, size distribution, homogeneity, variability in size, composition or density, etc.)

•

method of measurement (static scale, belt scale, flow meter etc.)

•

type of equipment being utilized (electromechanical, nuclear etc.)

•

design of the measuring installation

•

maintenance of the equipment

•

calibration of the equipment.

The objective of mass measurement for Metal ing is to provide a mass in which the random errors are reduced to within acceptable limits and bias eliminated. Errors are possible throughout the mass measuring chain, including those introduced by the equipment layout, the material being measured and mechanical, physical or environmental aspects, and by the measuring source such as a load cell, or by the various processes in the electronic circuits. The more steps in the chain, the greater the possibility of error and the greater the magnitude, as all errors are cumulative. The statistical methods to establish the precision and bias for mass measurement are discussed in Merks and Merks. In addition, there are the errors in the moisture or density measurements used to convert a wet weight or volume to a dry mass. In general, the larger the quantity being measured and the more variable in size and flow rate, the lower the accuracy. In addition, in the case of precious metals and base metals, the larger the stream or quantity of material, generally the lower the content of the constituent for which the metal balance is required, which affects the analytical precision. Thus, in the mining and metallurgical industry, generally the accuracy of mass measurement will increase through the processing chain from raw feed or ore to the final metal, as the materials become more homogeneous in size and composition, with a higher grade, and they are present in smaller quantities. The cost of the mass measurement device is often only a small portion of the cost of the total system required to obtain a mass flow measurement suitable for ing. A total system includes equipment for calibration, such as weigh tanks or bins, or other facilities for check weighing. However, the higher capital cost incurred due to correct design and installation of the system will often result in a lower total cost of

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ownership because of reduced maintenance and operating costs, while also increasing measurement precision.

3.2.1 Scales/Weighing Equipment In the weighing industry, if a scale is accurate, it has been properly calibrated, it conforms to the applicable standard and its performance is within the permitted tolerances (or maximum permissible errors) for initial verification and it is in service. Although the maximum tolerances specified in the Standards, such as NIST 44, may suggest 95% confidence levels, this is in fact not actually stated. Accuracy is generally expressed in the industry in of “inaccuracy” or the amount of error, and it is quoted as ± x%, usually of the full-scale value, without defining the confidence levels. The precisions quoted in this section are percentages of full-scale value. Often the number quoted in literature is actually precision and not accuracy. For further discussion of this see Merks15, and Colijn11. The figures quoted below are expected precisions in of a coefficient of variation. Static scales provide the most accurate and precise method of measuring mass and must be the choice, where circumstances permit, for custody transfer and Primary and Secondary Metal ing purposes. They can be easily certified by standardised procedures. Static scales include platform scales, road and rail weighbridges, gantry, hopper and bin scales. Precisions generally range between ± 0.05% to ± 0.2% for platform scales and ± 0.1% to ± 0.5% for weighbridges, on gross load measurements. For hopper/bin scales and gantry scales the equivalent figures are ± 0.1% to ± 0.25% and ± 0.15% to ± 0.4% respectively. The accuracy of in-motion weighing is affected significantly by the truck speed and acceleration. This effect is more apparent in the weighing of road trucks where the control of speed and acceleration is much more difficult than for rail. Thus, in-motion weighers are certifiable for rail use but not for road use. Certified in-motion weighing is a suitable choice for Primary Metal ing for rail transport, provided that the key principles of taring each truck or hopper on each occasion and controlling the speed are adhered to. In-motion weighing should give a precision of ± 0.5%. Certified electromechanical belt weighers must be the second choice for metal ing purposes after static scales or in-motion weighing, provided that the design, installation, calibration, operation and maintenance requirements are adhered to. The accuracy of conveyor belt weighing is installation specific and it is affected by:

•

belt effects such as tension, stiffness and misalignment of the belt or the idlers

•

flexibility of the weigh carriage

•

spillage onto the weigh section

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•

errors in the belt speed measurement

•

calibration errors and poor calibration techniques

•

errors in the other processes of the measurement chain

•

belt loading (proportion of design and variability)

•

changes in the material being conveyed

•

external environmental factors such as wind, dust and excessive vibration or shocks

•

poor operation, housekeeping and maintenance

The majority of the error in a belt scale has normally been found to be due to conveyor design and resulting belt effects and practices (Colijn2). If properly designed, installed, operated, maintained and calibrated they should achieve operating precisions of ± 0.5%. Nuclear belt weighers, although not subject to belt errors, are generally less accurate because of effects related to the material conveyed. Precisions of ± 1–2% can be achieved but, in practice, are often much worse. They should not be used for Primary Metal ing but they can be used for Secondary Metal ing if the material is homogeneous in size and composition and the belt loading uniform.

3.2.2 Flow Meters The accuracy of flow meters can be expressed in three different ways:

•

Percentage of span

•

Percentage of a rate or reading

•

Percentage of the upper range limit

The accuracies quoted below are those at 95% confidence levels. The accuracy of electromagnetic flow meters, especially on slurries, is installation specific. The accuracy is adversely affected by changes in the material and the fact that the density measurement introduces another source of error. Typical accuracies are ± 0.2 to ± 1% of reading and ± 0.5% is attainable on water or clean solutions if selection, design, installation, operation and calibration follow the applicable standards and manufacturer’s recommendations. On slurries, however, the accuracy is usually much worse than the figures quoted above for the reasons discussed in Section 3.4, and because they are seldom calibrated by material tests as recommended. Because of this, accuracies of only 5–10 % are common. Therefore, their use for Primary Metal ing is not recommended unless the use of static weighing is not possible. Coriolis mass flow meters are capable of high accuracy, ± 0.2% of reading in certain cases, but they only have limited application in the extractive industry, as they require low fluid velocity and they are sensitive to the quantity of entrained air. Ultrasonic transit time meters have limited applicability for specific applications and in those cases, they can give an accuracy of 0.5 to 2% of the reading. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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Momentum (impact) mass flow meters can be used for free flowing solids and they are capable of achieving an accuracy of ± 1.0% of the reading. Differential pressure type meters are not recommended measurements, as there are too many cumulative sources of error.

for

ing

METHODS OF MEASURING MASS

3.3

The various static and dynamic methods and equipment for measuring mass are well covered in the literature. Colijn1 (1983) describes the equipment and gives guidelines for selection, installation, operation, maintenance and calibration. Major suppliers provide similar information for their equipment. Similar information for the various types of mass and volume flow meters is given in various textbooks and publications. The various methods for measuring mass are listed below. Their applicability and the recommended guidelines or Standards are covered in the sections which follow.

•

Static Weighing (Platform, Truck and Rail Weighbridges)

•

Static Weighing (Weigh Hoppers, Bins and Gantry Scales)

•

In-Motion Weighing (Rail and Road)

•

Conveyor Belt Weighers ▪ ▪

•

Electro Mechanical Nuclear

Flow Meters ▪

▪

Volume (differential pressure, turbine, electromagnetic, variable area, vortex, positive displacement, thermal, ultrasonic – including Doppler and transit time differential methods) all of which require measurement of the density in order to determine mass flow Mass (momentum, impact, torque, Coriolis)

•

Survey Techniques (land or aerial, photogrammetry, laser)

•

Draught Surveys

Differential pressure type meters are generally not recommended for ing measurements, as there are too many cumulative sources of error. Flow meters, other than electromagnetic, Coriolis, momentum, or transit time types, only have very limited applicability for the measurement of flow in closed conduits in the mining/metallurgical industry. They are generally not accurate enough for ing purposes, especially on slurries, and therefore are not covered in this Code. All methods using volume or volume flow determination require the measurement of the bulk density of the material in the stockpile or bin, or the specific gravity (relative density) of the slurry or liquid. This can be performed by using a nuclear (gamma) gauge (Section 3.7.2), in a few cases by a penetrometer (Section 3.7.4), or preferably by bulk or laboratory tests (Section 3.7.1). The dry relative density of the solids and the density of the carrier, in the case of slurry flow which is often water AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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(which may have a density greater than 1.00), are also required. The mass flow is computed by multiplying the flow and density signals and integrating on a continuous basis However, density determination can be a cause of error. In the case of stockpiles, for example, although it is possible to measure the volume accurately, with sufficient time and effort and with modern survey techniques, it is difficult to determine the insitu bulk density and even more difficult to obtain a representative sample, especially if the pile is not homogeneous. This should be considered when stockpile surveys are performed.

3.4

MASS FLOW MEASUREMENT

3.4.1 Mass Flow in Rail/Road Trucks 3.4.1.1 Static Weighing Road/rail weighbridges and platform scales Material in, or being loaded into or unloaded from, rail or road trucks is usually weighed on static weighbridges or platform scales, as this is the most accurate method. The equipment is described by Colijn3. The method used is dependent on the quantity of material to be weighed, whether it is in identifiable batches (lots) and on the packaging method (drums, bags, bulk). In all cases the principles are the same:

•

The gross weight is established by weighing the full truck/container

•

The truck/container is weighed empty and the tare is established and then deducted (or the reverse of the these two steps for loading)

•

Representative samples in of recognized procedures are taken to determine moisture content by standard tests

•

The dry weight is calculated

Calibration and Certification These scales may be readily certified (assized), and in some cases have to be so by law or contract. For the purposes of Primary Metal ing they must be certified. The design, installation, operation and calibration are covered by International and National Standards and National Metrology Acts. For certification, these scales have to comply with certain design specifications, permissible acceptance tolerances or levels of accuracy when new, and maintenance accuracy levels for equipment in use, which define the limits of allowable error or departure from true performance or value, based on the defined test procedures as laid out in the Standards. These accuracy levels are defined for the various classes of scales (Appendix C.1.1.). They are calibrated by AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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Certified Weights traceable back to the relevant National Prototype Kilograms and thus to the International Unit of Mass. The test weights and the test scales to weigh them are in turn certified in of National Standards. The test equipment to weigh test weights has to be capable of a greater degree of accuracy than the commercial scales (Appendix C.1.1) National bodies or companies, who have been accredited by the national authority, perform the certification. The precision and bias can be evaluated by statistical techniques such as those detailed in ISO 3534.1. Hopper/Bin Scales and Gantry Scales Hopper/Bin Scales, either singly or in pairs, are also capable of good levels of accuracy although they require a more elaborate installation for loading or offloading of the materials. The equipment, design and installation guidelines and calibration techniques are described by Colijn4 and in literature available from the equipment suppliers. Gantry scales are usually used for bulk offloading of ship cargoes. These scales can be calibrated with certified test weights as for the platform scales and weighbridges and they can be certified by the relevant authorities.

3.4.1.2 In-Motion Weighing In-motion weighing is the weighing of materials in road or rail trucks or other containers, whilst the containers are in motion. This is usually used for large quantities such as bulk commodities transported either by road or by rail. The various types are described in Colijn5, who also lists installation guidelines and factors affecting the uncertainty of the measurement. The various possibilities for in-motion rail weighing are:

•

Single Draught Gravity: the weighing of an entire uncoupled rail car which is moving slowly by gravity down a slight decline. This requires a long weighbridge

•

Two-Draught Gravity: similar to the above, except that each bogey of the rail car is weighed separately and thus the weighbridge is shorter

•

Single Draught Coupled: the entire span of trucks moves slowly through the weigh area. All cars have to be the same length coupler to coupler and the weighbridge the same length as the cars

•

Two-Draught Coupled: same as the above, except that the weighbridge is shorter than the single draft coupled and each end of the car is weighed separately

•

Axle Weighing: same as the above, except that individual axles of the wagons are weighed, using a short rail section with strain gauges which means multiple weighings for each rail car (with the number being dependent on the bogey design)

The accuracy of in-motion weighing for rail trucks is dependent on: AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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•

the track layout at both ends of the weighing section (which should be straight and level)

•

the fastening of the rails on these approaches (in concrete)

•

constant speed over the weighing section (less than 8 km/hr)

•

good maintenance of the track and rolling stock to reduce impacts and vibration.

Train speed must be monitored automatically and, ideally, it should be controlled. Where applicable, special lightning protection must also be installed. In theory, the individual weighing of complete, uncoupled trucks should give the most accurate result. However, modern in-motion weighers normally weigh individual axles on a coupled basis, as this is simple, more rapid and practical to perform and the software can compensate for coupling and speed effects. The multiple weighing of individual axles provides a larger number of readings, which can assist in evaluating the results and improves the average deviation over a measuring period. For more accurate installations it is common to have four or eight strain gauges. Previously train speeds were limited to 8km per hour for certification but with the latest equipment it is possible to operate up to 20km per hour or higher. Rail in-motion weighers can be calibrated and certified according to various standards and national Trade and Metrology Regulations (in South Africa this is covered in Regulation 61) and used for custody transfer purposes. However, certification is not possible for road trucks at present because of the variable accuracy due to the effect of truck speed and the acceleration or deceleration, which is difficult to control. If the equipment is adequately designed, installed, maintained, calibrated, and operated (especially with respect to train speed), a precision of ± 0.5% should be achievable, assuming that the empty trucks or trains are also weighed on the system and the tare established on every weighing.

3.4.2 Mass Flow of Feed Materials, Products and Intermediates Products must be weighed by one of the static methods in order to obtain the highest accuracy. For material being packed into drums, bags, or other containers, the net weight must be established by measuring the tare (empty weight) and the weight after filling, by means of a platform scale. A bin scale delivering a preset weight may also be used. In addition, the dispatch gross weight is established, after loading the truck, on a weighbridge. As solid materials often contain some moisture it is important that samples are taken, preferably during loading or unloading, to establish moisture content. The mass flow of feed materials, or inter/intra plant intermediates (e.g. matte), required for Primary Metal ing, must be weighed in the same manner or by in-motion weighing, even if not required commercially, or by belt conveyor weighing, as specified below, with the weighing equipment certified, since these streams are critical for Metal ing. However, it may be decided, after due assessment of the risk to the company and an evaluation of the costs/benefits, company needs and practical limitations, that certification is unwarranted. In these cases, suitable inAMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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house procedures for calibration, operation and maintenance of the equipment, based on the appropriate standards and legal requirements, must be drawn up and applied and an Exception Report motivated and agreed in of Company procedures to permit such uncertified operation. Where a product is transferred from one plant to another, it is good practice for the mass to be measured at both loading and unloading points for on-going comparison and error determination.

3.4.3 Mass Flow on Conveyors Belt scales (weighers) enable the continuous weighing of large quantities of materials travelling on conveyor belts without the disruption to the process that batch weighing can produce. Belt weighers are divided into three accuracy classes (OIML R50-1 1997). These are shown below: OIML Class

Maximum Permissible Errors for the Calculated Load Initial Verification

In Service

0.5

± 0.25 %

± 0.50 %

1

± 0.50 %

± 1.00 %

2

± 1.00 %

± 2.00 %

As a minimum requirement, weighers conforming to the maximum permissible errors allowed in OIML Class 0.5, or an appropriate equivalent local national standard, must be used for Primary ing, trade, commercial and custody transfer purposes and they are the only weighers that can be certified (assized). OIML Class 1 (or equivalent) is required for Secondary ing and Class 2 for less critical applications such as plant control. Although no ISO standard exists, various other national Standards specify the allowable precisions for the various classes of these weighers and they are largely based on the principles in the OIML recommendation. It should be noted that some of these Standards may refer to a different number of classes or to no class, (there were only two classes in the previous edition of the OIML recommendation), with JIS B 7606 having six classes and the NIST 44 handbook only referring to a single class and specifying a maximum tolerance of 0.25% of the test load for both maintenance and acceptance tolerances on materialrun tests. It is important therefore, for certification, to ensure that national regulations are met, as specific requirements and specifications for testing can be slightly different. Static scales are in general more accurate than belt scales, and they are also capable of greater sensitivity, because they weigh larger quantities of material at one time. In addition, the accuracy of belt scales in operation is often considerably poorer than that expected, based on the specification of the scale, because of poor choice, system design, or bad operation or calibration.

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The weight recorded by the belt weigher is the wet weight, as most materials contain moisture. This moisture content is often variable and it is important that samples for moisture determination are taken at regular intervals using approved procedures, to enable an accurate moisture determination. Moisture for a few materials (fine, consistent) can also be determined continuously by a nuclear neutron gauge (Section 3.7.2) or a microwave gauge (Section 3.7.3). The former is applicable mainly to material in piles, bins and tanks and the latter to belts, chutes or conduits. Certification Certain makes and types of belt conveyor weighers and installations can be certified (assized) for trade (custody transfer) purposes, as they are, for example, in the supply of coal to power stations. Certification is installation specific and procedural requirements may vary from one country to another. For commercial and Primary Metal ing purposes, belt weighers must be certified in of this Code. Where, after an assessment of the specific application, material and importance of the process stream, the practicality and cost of achieving an installation of the required standard and the risk to the company, it is decided that certification is not warranted, an Exception Report must be motivated and agreed, by the Company’s approval procedure, to permit uncertified operation. However, even in these cases, the design, installation, operation and calibration should be in accordance with suitable and clearly defined in-house procedures, so that a maximum error of ± 0,5 % can be achieved and maintained.

3.4.3.1 Electro-Mechanical Weighers The components of these weighers and the sources of error are described by Colijn6, as well as the key factors for choice, design and correct installation (Colijn7). The factors affecting accuracy are discussed further in Section 3.2 The basic components of an electromechanical belt scale are the following:

•

The scale suspension (weighing) section on the conveyor which transmits the load on the belt to the load cells sensor(s)

•

The load sensor(s) which converts the load force to a form acceptable to the mass totaliser

•

The belt travel speed pickup which s the belt and transmits the belt speed to the speed sensor

•

The belt travel speed sensor which converts the belt speed to a form suitable to the mass totaliser

•

The mass totaliser which computes the mass travel over the conveyor scales and indicates and records that value

The scale suspension must be rigid with torsional stability and designed with minimal flat surfaces to reduce spillage build-up; it must also be capable of withstanding high temporary over-loads. The suspension may have a single weigh idler or multiple weigh idlers (up to 6 to 8) and it may be of various designs such as pivoting, floating, counterweight etc (Colijn2). Modern belt weighers are sometimes fitted with counterweights to counteract the weight of the empty belt and thus increase sensitivity. In general, more weighing idlers AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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improve accuracy. For Primary ing usually 4 to 6 weighing idlers are required to achieve the necessary accuracy. However, the choice is specific to the installation and two weigh idlers may be suitable in certain less onerous applications. The load sensors may be single or multiple load cells and either with direct bearing or with the load transmitted to them by a system of levers. Information on the calibration of load cells may be found in OIML 60 and BS 8422. The optimum choice is installation specific and scale suppliers have guidelines for selecting the best option, as well as for the correct conveyor layout and design, to achieve good tolerances in a specific application, according to the requirements of various Standards (both for new installations and for retrofits). The NIST requirements (Appendix C.1.3.) linked to OIML Recommendation 50 are generally used, however, various countries have their own requirements (for example in South Africa requirements are specified in the Trade and Metrology Act 1973 Regulation 47). The key requirements (Colijn7) for a conveyor belt weighing installation in order to obtain the accuracy required for certification are:

•

Conveyor should be close to horizontal and relatively short (less than 70 metres)

•

Ideally the conveyor should have no curved (catenary) sections, but if this is not possible the conveyor must not have a curved section between the loading point and the scale and not closer than 6 metres or 5 idler spacings beyond the scale if convex, or at least 15 metres to the curve tangent if concave

•

The conveyor should be in with the idlers on the scale and the three approach and lead idlers at all times

•

Conveyor must be endless with any ts being smooth (spliced)

•

Conveyor should be sized to assure at least 80% of volumetric loading capacity, the belt loading centred and the belt as light and flexible as possible for the intended service

•

Maximum troughing angle for the idlers should be 35 degrees (preferably 20)

•

Conveyor stringers through the weigh region should be continuous and of sufficient size to limit deflection and promote good alignment

•

Conveyor take-up should be of the automatic counterweighted type

•

Conveyor loading feeding system should supply as uniform and constant a load as possible, within 50-90% of the scale range

•

Conveyor weighers should be protected from wind, weather and extreme or rapid temperature variations

•

There should be no tripper on the conveyor

•

The idlers in the weigh region must be carefully aligned to the specified tolerances and the rolls within the specified eccentricity

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•

The scale should be located between 6-15 metres from the loading point

•

Side skirting or training idlers should not be closer than 6 metres to the weigh section

The layout of the weighing system and conveyor should be such as to permit calibration of the weigher with chains/ weights and the check weighing of the material by weigh hopper or weighbridge for calibration purposes. The accurate measurement of the belt travel speed is as important as the mass on the conveyor and any error is directly proportional to the mass flow. Various methods can be utilised. Their advantages and disadvantages are summarised in the literature (Colijn8). For Metal ing, belt speed measurement must be derived from a cylinder or wheel in with the belt itself. Calibration and Certification Various calibration methods exist, such as dead (static) test weights, calibration dynamically with an installed test weight, test chains of a certified weight per metre and the material-run (bulk test) method. The latter is preferred as it involves ing a known weight of the material, normally measured by the weigher, over the actual weigher in the normal operating mode and is the only acceptable method if the scale is to be certified. This requires facilities such as reference weigh bins or weighbridges to test weigh the material ing over the scale to a higher level of accuracy (± 0.1% according to NIST Handbook 44) than that required for the belt scale being calibrated. Calibration should cover a range of 20% to 100% of the belt loading (NIST 44). Test chains are the next most suitable method A new scale should be calibrated at the supplier before delivery, to establish the tolerance and the initial intrinsic error (the laboratory tolerance for many belt scales is often less than 0.1%). A new installation should then undergo an Acceptance Test on site to establish the intrinsic error. For certifying purposes, this should show a maximum error of ± 0.25 % (NIST 44 and OIML 50). Calibration according to recognised procedures gives the K-Factor which is the factor (bias error) resulting from comparing the result of a bulk test, to the theoretical calibration of the scale with test weights. This gives a measure of the installation effects such as belt tension. For correctly designed installations, this should be close to zero, which means that the scale will tend to be much more stable. To maintain accuracy, zero balancing should be performed frequently as well as a check with a static weight. The frequency of material-run or chain calibration is dependent on operating experience on the specific installation (periods of 3–6 months are common). For critical applications, two belt weighers are often installed in series. This provides a check on when any problems occur and the comparison can be used to calculate the precision of the measurement (Merks1). Certification has to be performed by an accredited company in of specific country legislation, which is normally contained in the relevant Trade and Metrology Acts, which, in turn, are usually based on OIML 50. If local AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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standards do not exist for certification, the Best Practice is to adhere to OIML 50. Most modern weighing installations are fitted with electronics for the integrator, that facilitate the calibration and resetting of the speed zero and span and the mass zero and span automatically, and accepting the instrument readings as correct. However, there is no substitute for fundamental checks of belt speed by timing a measured distance of travel and of belt loadings by calibration with check weights. All these results, both the as found and corrected, must be recorded and analysed to establish the operating accuracy of the weigher.

3.4.3.2 Nuclear Belt Weighers These use the principle of the absorption of nuclear (gamma) radiation to measure belt loading and the relevant national nuclear authorities control their use and maintenance. Suppliers provide guides to the selection of electromechanical or nuclear weighers for a specific application. They are simple to install and they are unaffected by belt effects, so the conveyor design is not as important as it is for electro-mechanical weighers. Nuclear belt weighers are also not susceptible to external forces such as wind, they are less maintenance intensive and easier to calibrate. However, they have the same operational limitations with respect to the measurement of belt speed, plant housekeeping, moisture determination and regular calibration. Most importantly, they are affected by variation in the size, composition and shape of the material being measured and they are therefore normally less accurate than electromechanical weighers, with maximum errors of ± 1 to ± 2% being achievable, but higher error levels being common. They can also be used, in certain applications, with nuclear neutron sources to determine moisture content. Because of the fact that their measurements are subject to errors from variations in the material being weighed, they should not be used for metal ing measurements.

3.4.4 Mass Flow in Conduits 3.4.4.1 Introduction For convenience and practical reasons, mass flow in closed conduits is often determined by measuring volume flow rate plus density or specific gravity in order to calculate the dry mass. This type of measurement is very common in the extractive industries for process flows and liquid and/or gaseous effluents. The determination of the relative density or specific gravity is discussed in 3.6. The degree of uncertainty in the mass flow is made up of errors both in the volume measurement, and in the density determination. There are over one hundred types of flow meters commercially available, operating on ten different principles. BS EN 7405 classifies flow meters into 12 different Groups (Strauss18). There is extensive literature available from suppliers and many technical papers (Appendix B.1.) and standards (Strauss19) on the selection of the correct flow meter for specific applications. Strauss20 gives a comparison of the performance of different types of flow AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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meters and Strauss21 shows the possible applicable areas for the different types of flow meters for the twelve Groups. However, in the metallurgical industry, many of the key process flows, in conduits, required for metal ing, are abrasive slurries containing a significant amount of solids. This effectively limits the choice of a suitable meter for these applications to an electromagnetic flow meter. In a few cases, with low flows and fine particles, it may be possible to utilise a Coriolis mass flow meter, or for low consistent solids contents, transit time ultrasonic flow meters may be suitable. For free flowing solids, momentum type mass flow meters may be used. Thus, this guide will concentrate on these four types. For relatively clean solutions, many types of flow meter will operate satisfactorily and give good accuracy. The measurement of gas flow, with or without particulates, for metal ing is normally required for off-gases and stacks, and it needs specialist equipment but is not covered in detail in this Code.

3.4.4.2 Specifications and Performance Often claims of accuracy for flow meters are ambiguous and misleading. The three essentials for performance specifications are:

•

Accuracy (total measurement uncertainty over the measuring range)

•

Measuring range

•

Traceability of the calibration back to a certified or approved flow measurement.

The total uncertainty is made up of that associated with the reference (the “true” value) plus that from the specific meter being tested. For flow meters used for metal ing purposes, these aspects are critical. Therefore, the uncertainty of flow measurement must be specified and the specifications must conform to ISO 11631. This standard defines the methods of specifying the flow meter performance and the different classes of traceability from Group A to F. For metal ing, this must be Group A if possible, or at least Group B.

3.4.4.3 Calibration and Certification For metal ing purposes the initial calibration and certification of the meter must have been performed in accordance with ISO 4185 or ISO 8316 (or national equivalents) in a testing facility accredited in of ISO/IEC Guide 25 or ISO 17025. This certification is carried out on water. The uncertainty should be assessed utilising ISO/TR 7066-1 and 7066-2 and ISO/TR 5168. For critical metal ing flows, such as thickener or flotation feed, where the measurement is used for Primary ing, the instrument must be calibrated regularly on the actual material in addition to the initial calibrations conducted in the test facility. Calibration can be performed in three ways:

•

Volumetric method

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•

Weight method

•

Comparison with a standard meter

However, field calibration is often imprecise as frequently, inadequate provision is made in the plant design and layout for calibrated weighing or volumetric tanks and for a diversion system. In addition, the system has to be accurately designed to ensure that volume or weight and the time of flow can be measured to a sufficient degree of accuracy (which should be 5 times better than that required for the meter). Although ISO 4185 and 8316 are intended for accredited testing facilities, the principles are directly applicable to in-plant calibration. It is also possible to remove the meters and test them at the various approved facilities available. However, there is, at present, no facility capable of testing the meters on slurries. This would also involve the transport of a large quantity of the material to the facility. In-series installation on site of a reference meter of proven stability and precision is a further possibility.

3.4.4.4 Solutions The measurement of the mass flow of solutions for ing purposes is mainly applicable in the industry to some process streams in the refining sections of the process, and then usually only for intra-company ing or for the measurement of effluent streams, to determine losses or conformance with permitted emissions. Differential pressure type meters will operate satisfactorily (ISO 5167-1 gives the specifications for geometry, operation and use), but at a lower level of accuracy than other types such as magnetic flow meters. Thus they should not be used for Primary Metal ing. The relevant standards, selection and calibration aspects are covered in the section below on slurries.

3.4.4.5 Slurries Electromagnetic flow meters Slurries in the metallurgical industry are abrasive and normally have a range of particle sizes, operating in the mixed-flow regime. Electromagnetic flow meters are the most suitable for this application, providing the fluid is conductive. They are available in a wide range of sizes and materials of construction to suit the application. The measurement is unaffected by pressure, temperature or viscosity. The measuring tube has no moving parts and there are no restrictions to the pipe cross section. However, they can only be used on conductive liquids and they are affected by changes in the mineralogy, particle size, velocity and magnetic constituents in the slurry. Meters of special design are available to handle these magnetic materials and they give satisfactory results, providing the magnetic content of the slurry is reasonably constant. Electromagnetic flow meters are capable of giving an accuracy of ± 0.5% of reading or better on water or clean solutions, providing they are installed correctly and calibrated according to standard procedures. For slurries the accuracy is generally lower. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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Flow meters should be manufactured in accordance with an international standard. Standards (ISO 6817, ISO 9104:1991, ISO 13359, ASME MFC16M-1999) for the installation, calibration, and methods of evaluating their performance must be adhered to. Manufacturers also provide recommended parameters for installation. It is important that safety and health considerations are taken into in the installation to allow safe operation, maintenance and calibration. Coriolis Mass Meters These meters measure mass flow directly by measuring the Coriolis force that is developed when mass flows through an oscillating tube. They eliminate the uncertainty of the density measurement, they are capable of an accuracy of ± 0.2% of reading and they require little space. However, they are relatively expensive, unavailable in large sizes at present and, because of geometry and erosion in the case of slurries, they can only be applied where the fluid velocity can be kept under 2 m.s-1 and the products are relatively fine. A possible application is for the measurement of flow of mineral concentrate. Note should be taken of the fact that the accuracy of these meters is badly affected by entrained air. Coriolis meters also give a measurement of the density of the fluid. Standards (ISO 10790:1993/2003, ASME MFC-11M2003) give guidance on the selection, installation and use of Coriolis meters and these standards should be followed. The general comments above on certification and calibration are also relevant. Ultrasonic Transit Type Flow Meters This method (ISO TR 12765) utilises the fact that a fluid directly influences the propagation velocity of sound waves within it. They can be fitted to the exterior of pipes thus making retrofitting simple. If the fluid is homogeneous they are unaffected by the physical properties. However, they require a uniform flow profile and the accuracy is strongly dependent on the acoustic transparency of the fluid and it ranges from ± 0.5 to ± 2% of full scale. Thus applicability for Metal ing purposes is limited. Calibration If used for critical metal ing flows, such as concentrate, thickener or flotation feed, because static or conveyor belt weighing is not possible, the flow meter must be calibrated regularly on the actual material as outlined in 3.4.4.3.

3.4.4.6 Solids Impact mass flow meters can be utilised for free-flowing solids and they operate by measuring the momentum of the solid stream impacting on a measuring plate. They are described in Colijn9, who also discusses their applicability and limitations. They are unaffected by the density of the material and are pre-calibrated by the manufacturer for the . Calibration checks are with test weights and do not require material tests. They measure wet mass flow and can give accuracies of ± 1%.

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3.4.4.7 Gases and Dusts The mass flow of gases and dusts is required to measure the losses of valuable materials in the dust, or the particulate content and the mass of pollutant gases such as SO2 or NOX, from stacks or fugitive emissions into the atmosphere, in order to measure conformity to environmental, health and safety provisions. The valuable component is normally very small (less than 1% of input) and thus errors resulting from the measurement are seldom significant in the overall metal balance calculation. However, because of the risk to a company’s reputation from unauthorised emissions or from exceeding the legal regulations and the emphasis on the triple bottom line, these measurements are important. Mass balancing for Environmental and Health needs is discussed further in Section 7.7.1.

3.4.5 Mass Flow in Open Channels For metal ing, the measurement of flow in open channels would usually be required for the measurement of effluent volumes to determine losses, which are usually a small percentage of feed, or to check conformity with environmental regulations and permits. Normally the solids content is small and the mass would be derived from separating and weighing the solids from the samples taken for analysis. An exception is concentrator tailings that are usually pumped in closed conduits. This loss may be significant (5–20% of the feed) and the stream critical to Primary ing. However, because of the difficulty in measuring the flow rate accurately, the mass flows are often calculated by the two- or three-product formulae, from the mill or float feed and concentrate mass flows and analyses. This practice must be motivated by way of an Exception Report. For the same reasons as given in 3.4.4.7, the measurement and ing for certain trace elements and potential pollutants has great significance for a company’s reporting. This is discussed further in 7.7.2. ISO/TR 8363 gives the guidelines for the selection of appropriate methods and lists 28 different methods for measuring flow in open channels that include velocity-area methods, weirs and flumes. This standard also gives the expected degree of uncertainty for the various methods varying from ± 10% for some to ± 1% for a thinplate, V-notch, sharp crest weir (ISO 1438-1) and it lists the various ISO standards applying to all the different methods. Thin plate weirs must be used for ing, for process streams such as effluents, as they are the most accurate form of measurement for this purpose.

3.4.6 Mass Flow for Bulk Commodities Mass measurement for bulk commodities such as coal, bauxite, iron ore, or base metal concentrates is normally carried out by static, in-motion or conveyor belt weighing, to the degree of accuracy required in of the contracts of supply and to meet the requirements for custody transfer. Thus, the weighers must be certified.

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These materials are often shipped direct in bulk from the supplier to the port, loaded onto a bulk carrier and unloaded at the consumer’s port for onward supply to the treatment facility. Draught Surveys In these cases, a method commonly specified and accepted for material being transported by bulk carrier is that of the draught survey. The draught survey is a mass measurement based on converting a difference between the displacements of a vessel under different loading conditions into a wet mass for a partial or full cargo. A suitably qualified marine surveyor, who operates in of accepted formulae, performs the survey. A reasonable accuracy is obtainable for full cargoes but partial cargoes or parcels are susceptible to a higher degree of uncertainty. Accuracy is also affected by external factors such as water density, currents, wind and waves and the skill of the surveyor. There is not, at this stage, any ISO standard covering draught surveys but, in some cases, the UN ECE Code for Coal Cargoes is used. There is also no degree of traceability for the survey technique. This should be contrasted with a certified belt or other type of weigher, the calibration of which can be traced back through a hierarchy of testing to internationally accepted weight standards. Marine surveyors quote an accuracy of ± 0.5% for full cargoes. Merks (1992) has suggested, in an analysis of the precision of some draught survey data that the coefficient of variation varies from 1% for full cargoes to 3% for partial cargoes. This should be compared with that achievable from static scales or more advanced belt scales as discussed elsewhere in this Code. On critical measurements, it is also possible to improve the quality of the mass measurement by installing two belt weighers in series and comparing the results continuously. However, the method of mass determination for these custody transfers is usually specified in the sales contract and in many cases, this is still the draught survey. Even where static or belt conveyor weights are accepted, the draught survey is performed as a check and for shipping purposes.

3.4.7 Mass in Storage Areas Mass in storage areas can be a significant quantity and it can be material to metal ing. It is usually difficult to determine to a level of accuracy sufficient for ing purposes, and it can conceal mass measurement and ing discrepancies if it is measured only by difference and it is not cross checked by other means such as reducing the stock to zero.

3.4.8 Mass in Stopes/ Ore Blocks At this stage, this is not included in the Code, as the starting point for metal ing has been taken as the ore feed to the treatment plant. However, the mass measurement and metal content at the treatment plant must be tracked back to the shaft or open pit and reconciled with other mass measurements for the broken ore, with surveyed measurements for the in-situ rock mined, (utilising the in-situ rock density) and any stocks of broken ore underground or in the pit.

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3.4.9 Mass in Stockpiles Measurement of mass in stockpiles is subject to a high degree of uncertainty. It is far better to sample and weigh the input and output, which can usually be performed to a higher level of accuracy, and calculate the contents of the stockpile by difference. The recommended practice for stockpile assessment is to have a system of dual parallel piles, which must be emptied at regular intervals. It is possible to measure the volume of the stockpile to an accuracy of better than ± 5% using photogrammetry or computerised automated laser survey techniques, which provide a 3-D image. The accuracy is dependent on the number of survey points taken and these measurements have to be performed by qualified surveyors. However, the measurement of the bulk density of the material in the stockpile, the moisture content (where practical, in the case of higher value materials such as concentrate, it is better for the stockpile to be under cover), and obtaining a representative sample is much more difficult. The bulk density of many materials is changed by compaction, ageing, swelling or oxidation and it is also affected by stockpile practice such as trucks driving over the pile and the method of dumping. Methods of measurement of bulk density are discussed in Section 3.7. If the material is not homogeneous in size and content, sampling of a stockpile to the accuracy desirable for metal ing, is almost impossible. The uncertainty of the metal content could be as poor as ± 15%; the only time when a stockpile value is correct is when it is empty. In the case of bulk commodities, such as coal or iron ore, where the material is reasonably consistent, and where the contents are large and material to the operation, a survey may be useful as an independent check on other tonnage measurements. For other stockpiles, such as ore or intermediates, it is suggested that accurate surveys should be performed only when measurement in and out is not possible, or the value of the stockpile is material to the company’s results, or the content of the stockpile has changed significantly over the ing period for some reason, and to fit in with reporting periods. The accuracy of the survey is dependent on the number of survey points taken, which should be tailored to suit the expected accuracy of the other variables and the importance of the value. International Standards do not exist and the accuracy of the survey is dependent on the skill of the surveyor.

3.4.10 Mass in Tanks and Bins The measurement of mass in tanks or bins suffers from the same problems as those found in stockpiles. Although the volume can be calculated reasonably accurately from surveys, the tank or bin geometry, the level, the measurement of density and sampling are all problematic. In the case of slurries there is also the possibility of dead volumes, solids build-up or the concentration of the specific component (e.g. gold) in some areas. For the purposes of metal ing, often it is the change in stock or inventory that is significant. If the metal content is critical, such as in the PGM or gold industry,

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then the only solution is to empty the tank or bin for the periodic stock-take. Use of parallel processing lines or storage facilities can be used to assist in this.

MASS IN PROCESS INVENTORY

3.5

The effort and resultant accuracy to determine these are dependent on the quantity and value relative to the company’s income. Thus in the case of precious metals it is essential on an annual basis, in the final refining operation, to use a method similar to the bubble method to both check the material and empty the equipment in the various stages. This assumes added importance if mass balance calculations show significant abnormalities.

3.5.1 Transient This is defined as process inventory (or lock-up) that is of short-term duration because of some process disturbance and/or where the mass flow through is continuous and live. It is normally contained in process vessels and equipment such as mills, furnaces and reaction vessels. This can be estimated and calculated with the same caveats as for tanks and bins above. It is possible that the ing metal can be present in more than one phase (e.g. solid and liquid, concentrate, matte and slag) in which case estimates must be made for all phases. Normally, estimates are made monthly, with periodic stock-takes to a more accurate level to suit company reporting.

LONG TERM

3.6

Long-term process inventories are those that will remain for years, or the life of mine or of that particular piece of equipment, and they include such items as:

•

The dead load in silos

•

The gold in mill liners

•

The build-up in furnaces.

This inventory can be subdivided into:

•

That which is absorbed or trapped in the process, such as gold into concrete foundations, which can only be recovered when the plant is dismantled and demolished (permanent inventory)

•

That which is recovered at regular intervals such as when mills are relined or furnaces re-bricked (semi-permanent inventory).

Measuring and asg quantities to the former is very difficult and must be avoided. (However, a “rule of thumb” figure used for early generation South African gold plants of 0.3% of total gold input over the life of Plant has been confirmed by clean up on closure). Quantities for the latter must be quantified and updated as the inventory is periodically removed and this information carefully documented for future use.

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DENSITY AND MOISTURE MEASUREMENT

3.7

The importance of density and moisture to mass measurement has been highlighted in previous sections. In this context the Code refers to moisture that can be removed from the sample by drying at temperatures up to 105 oC, as water can be present in the material in different forms including the water of crystallisation. In the Coal Industry, for example, the following are examples of the various types of moisture that may be present in a sample:

•

Free moisture

•

Surface moisture

•

Residual moisture

•

Inherent moisture

•

Equilibrium moisture

•

Moisture holding capacity

•

Total moisture

•

Moisture in the analysis sample

•

Hygroscopic moisture (lignite)

•

Moisture of constitution

It is important to ensure that samples for moisture determination are taken in such a manner, as specified by the Code, to ensure that the moisture in the sample is representative of the material being weighed and that no evaporation can take place, or water ingress occur, before the determination takes place. It is also very important to ensure that any analysis is carried out on the sample that has been used for moisture determination, or an appropriate correction made, in order that the analytical result can be related directly to the dry tonnes value. The measurement may be:

•

Measurement of moisture in the material in road/rail trucks or other containers

•

Measurement of the moisture content in material on a conveyor belt

•

Measurement of moisture content of a material in bins/hoppers or stockpiles

•

Bulk density of material in bins or stockpiles

•

Specific gravity (SG)/relative density of slurry flowing in a conduit or in a tank or other vessel

•

Relative density of the solid and liquid components of slurry

The uncertainty in the density measurement has a direct effect on the resultant calculated dry mass and it can be large, as for example in the case of low SG slurries. At an SG of 1.25, a 2% error in the density measurement translates into a 10% mass flow error, assuming that the carrying fluid has an SG of 1.00. In operating plants, this fluid is often recirculated water, which may have a varying SG. If the SG is 1.05 then the error in the above example will increase to 12.5%. Thus there is no point in AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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measuring the volume flow to ± 0.5% if critical attention is not paid to the density measurement, including both the solids and solution.

3.7.1 Bulk and Laboratory Tests The most precise and preferred method for measuring relative or bulk density, is by bulk, laboratory or plant tests whereby an accurately known volume is weighed and the bulk/solid/liquid/slurry density is then calculated. The volume of solids can be determined by various methods including displacement of liquid. For determining moisture, a known weight of material is dried under defined conditions and the loss in weight due to moisture is calculated. This can be applied to bulk ore, stockpiles (test pits) and conveyor belts. The degree of uncertainty is mainly dependent on correct sampling and sample handling. Great care must be taken in the sampling of slurry streams. For example, the precision of the measurement derived from small specific gravity flasks is usually poor. In the case of stockpiles, the water test can be used whereby pits are dug in the stockpile, the material removed and accurately weighed. The pit is lined with an impervious membrane and filled with a measured volume of water. The number and location of the pits is decided by sampling theory. In some cases historical information based on previous measurements and tests can be applied, including analyses and mineralogy of the material, but it should be regularly reviewed and re-measured.

3.7.2 Nuclear Gauges These use neutrons or gamma rays. Neutron gauges can be used to measure the moisture content of some materials in tanks or bins or on a conveyor belt. They measure the hydrogen content of the material from which the water content can be calculated. As they measure total hydrogen content they must be calibrated and corrected for materials that contain hydrogen in forms other than water. If used in conjunction with gamma belt weighers on a conveyor belt, which will give the total wet mass of the material, the dry weight can be determined. They are limited to use with relatively fine and homogeneous products. Accuracies of ± 1.0% are claimed for wood chips at 50% moisture and ± 0.5% for concentrate (Colijn10). Nuclear gamma gauges are also used for measurement of the bulk density in stockpiles or heaps but can only penetrate to about 30 cm. The most common use is for density measurement of slurry flow in conduits, used in conjunction with an electromagnetic flow meter. The correct choice and accuracy is installation specific and guidelines supplied by the vendors must be adhered to. The initial calibration must be performed in the field at two points on the scale by measuring the weights of known volumes of water and slurry ed through the meter. Accuracy is affected by many of the similar aspects that affect flow meters. It is essential that the relative densities of the solids and the carrier solution are checked periodically and that the calibration be checked regularly, by correctly handtaken samples for SG. The meters themselves are capable of ± 1% accuracy, but this is often not obtained in plant conditions, due to poor installation, inhomogeneity and particle size changes. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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3.7.3 Microwave Gauges These can be used in similar applications to those for which neutron gauges are suitable. They operate on the principle that the intensity and speed of propagation of microwaves is influenced by free water molecules. The change in intensity and velocity of the waves is directly related to the moisture content in the material.

3.7.4 Penetrometers These instruments have limited use to determine the bulk density of stockpiles of material such as coal of a relatively fine uniform size, by measuring the force required to penetrate a cone to a known depth. They require a skilled operator and cannot be used on stockpiles with varying size material or containing rocks, such as ROM ore.

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4

SAMPLING

INTRODUCTION

4.1

Within the mineral and metallurgical industries, process streams are very often in the form of a flow of coarse broken ore or slag, or in the form of a flow of slurried finer material. Metal ing balances for such processes demand that these process flows are sampled accurately with precisions that will cause the accuracy of the final balance to fall within set target range(s). The ranges, into which the balance uncertainties for the various target elements or chemical species of interest fall (referred to in what follows as a measurand), are prescribed by management considerations of the financial risk associated with the uncertainties. These uncertainties must be acceptable to any stakeholders in the operations as well as to outside bodies that are prescriptive of corporate governance standards. The accuracy of the final result of a sampling of a process flow for a given measurand depends on four factors:

•

The ‘correctness’ of the sampling device

•

The presentation of the process flow to be sampled to the sampling device

•

The accuracy of the analytical method employed to arrive at the final analysis assigned to a sample

•

The heterogeneity of the material to be sampled and the design of the sampling protocol, including the sample preparation protocol

There are two types of heterogeneity associated with a process flow: Intrinsic heterogeneity (IH) which is determined entirely by the size distribution of the solids and the variation of the concentration of the measurand from one particle to the next, within the various size fractions of the solids Distributional heterogeneity (DH) which depends on the variation of the total measurand concentration from one moment to the next, as the process flow es the sampling point. In most cases, with appropriate investment in equipment and the correct arrangement of the process layout, it is possible to present the process flow to the sampler and to take ‘mechanically correct’ sample increments from the process flow, so that the sample formed by the collection of the increments is unbiased. This will invariably not apply to sampling of run of mine ore because of the difficulties presented by the range of, and variations in, the size and grade of individual particles, moisture, and the problems associated with the design of a multi-stage sampling system to generate an unbiased laboratory sample from the primary sample. The accuracy of the sample (the variance between the true average analysis of the process stream over the sampling period and the final analysis of the sample) depends on the heterogeneity of the process flow, the accuracy of the analytical method and the number and mass of the increments taken from the flow.

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4.2

SAMPLING ERRORS

As described by Gy (1982), Pitard (1993) and Holmes (2004), amongst others, the total sampling error (TE) of a particular sampling stage may be divided into contributions from a number of independent components as shown in the following equation: TEi = FEi + GEi + QE2i + QE3i + WEi + DEi + EEi + PEi

[Eqn 4.1]

where FE = Fundamental Error; GE = Grouping and Segregation Error; QE2 = LongRange Quality Fluctuation Error; QE3 = Periodic Quality Fluctuation Error; WE = Weighting Error; DE = Increment Delimitation Error; EE = Increment Extraction Error; PE = Preparation Error. In of the total error in metallurgical ing, the error due to the mass measurement including moisture determination, and the error occurring during the analysis of the measurands, must be added to this equation. In addition, as discussed by Holmes (2004), the components in the equation apply principally to sampling from moving process streams and, therefore, not all of the components apply to every sampling situation. For example, long-range and periodic quality fluctuations and weighting errors do not apply to blast hole sampling. Holmes also makes the point that delimitation error, extraction error and preparation error require special attention, because they can easily introduce bias into the measurement process. However, this can be minimised with careful attention to the installation and operation of correctly designed sampling systems. The other components are largely random errors that can never be completely eliminated, but they can also be reduced to acceptable levels by careful design of the sampling system. Delimitation errors are caused by the incorrect geometrical definition of the sample increment, (e.g. a parallel cutter opening in a rotary sampler) and they can be eliminated if all parts of the stream’s cross-section are diverted by the sample cutter for the same length of time. Increment extraction errors occur when a sample increment is not fully extracted, (e.g. part of a process stream is missed by the sample cutter), and these errors can be eliminated by ensuring that the increment is completely extracted without any loss of sample. Weighting errors are experienced when sampling a moving stream that has a high variation in flow rate. It is not usually a major problem (assuming that both the minimum and maximum flow rates are correctly sampled) and they can be overcome by regulating the flow rate prior to sampling. As a general rule, the weighting error is negligible for variations in flow rate up to 10%, and acceptable for variations in flow rate up to 20%. Careful sample preparation practice will ensure that the preparation error PE is minimised. The potential effects of long-range and periodic quality fluctuations can be established by conducting variographic experiments as described by Gy (1979) and

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they are minimised by taking frequent increments based on a random stratified sampling scheme. Having minimised all of these errors, Equation 4.1 reduces to: TE = FE + GE + QE2

[Eqn 4.2]

And the sampling variance is given by: σ2S = σ2FE + σ2GE +σ2QE2

[Eqn 4.3]

These variances can be determined from a variographic experiment in which the quality characteristics of at least 30 individual increments, taken from a lot, are determined in duplicate. The differences between increments at increasing intervals, called lags, are calculated. A plot of the results is called a variogram. The value of the variogram VE(t) corresponding to a lag of t increments is given by: VE(t) = Σ (xi+t – xi)2 2(n-t)

[Eqn 4.4]

where: xi = the value of the quality characteristic for increment i for i = 1, 2, 3, , n n-t = the number of pairs of increments at lag t apart The resultant variogram is called the ‘experimental’ variogram and it includes the variance of sample preparation and analysis as well as the sampling variance. To give the ‘corrected’ variogram, which provides information on the sampling error only, the sample preparation and analysis errors must be subtracted. Variograms can usually be approximated to a straight line, especially over small values of t. It can, therefore, be assumed that: VC(t) = AC + Bt where:

[Eqn 4.5]

AC = the random component of variance of the corrected variogram B = the slope of the variogram

The sampling variance for systematic and random stratified sampling for mass basis sampling is then given by the following equations: Systematic Sampling σS2 = AC + BmL n 6n2

[Eqn 4.6]

where: n = the number of increments AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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mL = the total mass of the lot Random Stratified Sampling σS2 = AC + BmL n 3n2

[Eqn 4.7]

For time basis sampling, mL is replaced by tL, the total time for sampling the lot. The first and second of these last two equations, correspond to the short-range and long-range quality fluctuations respectively. For systematic sampling, therefore: σQE12 = AC n σQE22 = BmL 6n2

[Eqn 4.8]

The variogram intercept AC is made up of two components:

•

The segregation and grouping variance σGE2

•

The fundamental error variance σFE2 for the particular increment mass used for the variographic experiment

The fundamental error variance results from the particle to particle variation in quality of the material making up the increment and it can be determined as described in 4.3.. As described by Gy, the fundamental error variance is proportional to the cube of the nominal top size of particles in the sample and inversely proportional to the increment mass. The variogram intercept AC can, therefore, be expressed as: AC = σGE2 + AFd3 ml where:

[Eqn 4.9]

AF = a constant d = the nominal top size of the particles in millimetres ml = the increment mass

A worked example of the calculation of a corrected variogram and the parameters derived from it is given in Appendix B of the Australian Standard AS 2884.1–1997. The relative variance due to intrinsic heterogeneity is equivalent to the fundamental sampling error (FE) and the following sections focus on the intrinsic heterogeneity of the solids, the manner in which the heterogeneity can be quantified and/or measured, and the impact that this heterogeneity has on every stage of sampling and sample preparation.

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4.3

STATISTICAL BASIS OF SAMPLING THEORY

The theory of sampling of particulate solids is based on a simple concept of how the numbers of particles of different size and composition vary from one nominally identical sub-sample of a material to the next. A conceptual ‘model’ of a heterogeneous particulate material must be developed to understand how the statistical concepts come together to provide results useful to sampling practice. It must be imagined that the material is separated into various size fractions by sieving or some other suitable method, and, that for each size fraction, the particles are sorted into composition classes, as illustrated in the diagram below, and the particles within each size and composition class are assumed to be identical. The amount of detail concerning the particles can always be improved by increasing the number of size and composition classes. Composition Size v

>

1

2

3

4

.

.

.

Nc

1 2 3 4 . . 1

.

2

3

4

5

6

7

Class

Ns

Figure 4.1 Size and composition classes

The particles in the i

th

size and j

th

composition class are taken to have a volume:

⎛ d i3 + d i3+1 ⎞ Vi = f ⎜ ⎟ 2 ⎝ ⎠

[Eqn 4.10]

d

d

where i and i +1 are the sieve apertures defining the i th size fraction and f is a shape factor than can be taken to be 0.5 for blocky particles. The particle density is denoted by

ρ ij

which gives them a typical mass of

of the measurand in the particles is denoted as

a ij

mij = ρijVi

. The concentration

.

With the definition of these values for the various size and composition fractions, it is

nij

, of then possible to describe a sample of material if the numbers of particles, each type are known or specified. The measurand concentration in the sample as a whole is:

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8

9

10

Ns

as =

Nc

∑∑a i =1 j =1 Ns Nc

ij

m ij n ij

∑∑m i =1

j =1

ij

n ij [Eqn 4.11]

Where Ns is the total number of size classes and Nc is the total number of composition classes. and the sample mass is:

Ns

Nc

M s = ∑ ∑ mij nij i =1 j =1

[Eqn 4.12]

In order to understand the statistics of sampling of a lot of material, it may be imagined that a substantial mass of the material is collected and each particle in the lot is given a number starting at 1 and going up to some very large figure N. To ‘sample’ the material, a set of S numbers is drawn entirely at random, from the numbers 1 to N, and the particles whose numbers are drawn are the ones taken as the ‘sample’. By selecting the particles in this way, there is no bias in favour of particles of any one type. In fact, this ‘ideal’ method of sampling can be considered as a standard against which any other method of particle selection for sampling can be compared. This method is the most equitable possible method of selecting particles for a sample, since the numbers of any one type of particle taken into the sample do not depend on the numbers of particles of any other type taken into the sample, and it is often referred to as the ‘equiprobable’ sampling method in discussions about sampling.

4.3.1 Intrinsic heterogeneity of a particulate material To understand how the numbers of particles in a sample taken from a process stream vary when a sample cutter is moved through the stream, (for example, at a conveyor transfer point), it must be recognised that the number of particles taken into the sample in consecutive motions of the cutter will vary, even when the mass flow rate of the process stream is constant, It follows from this description of the sampling process that the number of particles of any one type that arrive in a sample follow a Poisson probability density. A number following a Poisson distribution is characterised simply by its expected or mean value. The expected value need not be a whole number, but the number of particles must of course be integers. The important aspect of the Poisson density is that the variance of the distribution is equal to the expected value.

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This concept of the number statistics of the particles is all that is required to understand how the concentration of the measurand varies from one nominally identical sub-sample to the next. In the formula for the measurand concentration of a sample, the number of particles of any one type in the sample follows a Poisson distribution with an expected number of particles

n

; the numbers vary

ij

independently. Using simple propagation of variance rules, it is possible to show that:

1 Var {a S } = M s2

Ns

Nc

∑ ∑ ( aij − a L ) m ij2 nij 2

i =1 j =1

[Eqn 4.13]

where the nominal measurand concentration in the sampled material (the ‘lot’) is: Ns

aL =

Nc

∑∑a i =1 j =1 Ns Nc

ij

∑∑m i =1 j =1

Dividing through by

aL

mij nij ij

nij [Eqn 4.14]

in Equation 4.13 gives:

⎧a ⎫ 1 Var ⎨ S ⎬ = 2 ⎩ aL ⎭ M s

2

⎛ aij − aL ⎞ 2 ⎜ ⎟ mij nij ∑∑ a i =1 j =1 ⎝ L ⎠ Ns

Nc

[Eqn 4.15]

This formula in of the particle numbers is not very convenient to use. It is preferable to restate the formula in of variables that are more familiar. For the nominal analysis of the material, the mass fraction in the I th size fraction is denoted as

Xi , and the mass fraction of material in the j

size fraction as

Yij

th

composition class within the I

th

. It is then a simple matter to convert Equation 4.15 to the form:

2

⎧aS ⎫ ⎛ aij − aL ⎞ 1 Var ⎨ ⎬ = ∑XV ⎟ i i ∑Yij ρij ⎜ a M a j=1 S i=1 ⎩ L ⎭IH ⎝ L ⎠ Ns

Nc

[Eqn 4.16]

This formula relates the relative variance (the square root of the left hand side is the relative standard deviation, expressed as a fraction rather than a percentage) of the measurand concentration between nominally identical samples of mass M s to the size distribution and the composition distributions within each size fraction. At a given state of comminution, the size and composition distributions completely characterise the material particulate heterogeneity with respect to a particular measurand. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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It is convenient to define a quantity, Ks , which may be called the sampling constant for a material, taking:

⎧a ⎫ K Var ⎨ S ⎬ = s ⎩ aL ⎭IH M S where

⎛ aij − aL ⎞ Ks = ∑ X iVi ∑Yij ρij ⎜ ⎟ i =1 j =1 ⎝ aL ⎠ Ns

Nc

2

[Eqn 4.17]

The relative variance due to intrinsic heterogeneity is also called the ‘fundamental sampling error’ (or variance) by Gy (1979). The sampling constant captures all the necessary information about the intrinsic heterogeneity of the material to be sampled. Since the variance is a relative variance, it is dimensionless and the sampling constant has units of mass. If the particle volume is calculated in cm3, and the particle density has units of g cm-3, the sampling constant will have units of g. To determine the sample mass that must be retained to ensure that the relative standard deviation (RSD) between nominally identical samples does not exceed a fraction r, then:

Ms =

Ks r2

[Eqn 4.18]

If r = 0.02 (2% relative), then:

Ms =

Ks = 2500 K s 0.0004

The sampling constant ‘sets the scale’ of sampling for a material. It is a quantitative measure of the material intrinsic heterogeneity with respect to a particular measurand. It is for that reason that the relative variance is given the subscript IH. It should be noted that a material can be very heterogeneous (large sampling constant) with respect to one measurand and nearly homogeneous with respect to some other measurand. For example, a low-grade gold ore of the appropriate mineralogy will have a low sampling constant with respect to silica and a relatively large sampling constant with respect to gold.

4.3.1.1 Practical use of the sampling constant Knowledge of the sampling constant for a material permits the calculation of the expected variance between nominally identical sub-samples of a given mass. This means that if some mass of material is put through a rotary sample divider and nominally identical sub-samples of mass M s are formed, the variance in the true analyses of the sub-samples with respect to a measurand cannot be less than the value calculated from the sampling constant. In practice, if the sub-samples were all prepared for analysis with great care, the variance between the analyses from each sub-sample would AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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be larger than the value calculated from the sampling constant, because the analytical results would also involve a component of variance due to the uncertainties in the analytical procedure. Knowledge of the sampling constant is also critical to the design of sampling systems. For example, if a sampling system is considered in which primary increments are taken from a process stream over the duration of a shift, each primary increment is then sub-sampled within the sampling system immediately after it is taken, the increments taken by the secondary sampler are collected and at the end of the shift they will have a mass Ms . In the case in which there is no distributional heterogeneity in the process stream (the concentration of the measurand does not change during the shift), the relative variance between the true value of the average measurand concentration in the process stream over the shift and the true measurand concentration in the sample cannot be less than K s M s . In real , the variance will be greater because there will be some distributional heterogeneity. The variance component due to distributional heterogeneity can be denoted as var [ a s a L ]DH and the total relative variance between the

true value of the average measurand concentration in the process stream over the shift and the true measurand concentration in the sample will be the sum:

⎧a ⎫ ⎧a ⎫ ⎧a ⎫ = var ⎨ s ⎬ + var ⎨ s ⎬ var ⎨ s ⎬ ⎩ a L ⎭ total ⎩ a L ⎭ DH ⎩ a L ⎭ IH

[Eqn 4.19]

4.3.1.2 Variance increase due to mass division of a sample

Ms is then taken to a laboratory and reduced in mass by careful rotary division, after drying, retaining a mass M s1 . This step

The shift sample of mass

of mass division introduces an additional component of uncertainty between the process stream measurand concentration and the true analysis of the sub-sample of mass

M s1 .

The increase in the relative variance is given by:

⎡ 1 ⎧a ⎫ 1 ⎤ ∆ var ⎨ s ⎬ = K s ⎢ − ⎥ ⎩ a L ⎭ IH ⎣ M s1 M s ⎦

[Eqn 4.20]

This means that the new variance is:

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⎡ 1 ⎧a ⎫ K 1⎤ var ⎨ s ⎬ = s + Ks ⎢ − ⎥ ⎩aL ⎭IH1 Ms ⎣ Ms1 Ms ⎦ K = s Ms1

[Eqn 4.21]

If the sample mass division is not carried out by rotary division, the variance between the process stream measurand concentration and the true analysis of the sub-sample will be larger. The sample may have been segregated before the mass division, and, as a result, the smaller sub-sample may end up being biased. Essentially, the sample, due to segregation, may have acquired some distributional heterogeneity (DH), and the sub-sample after the division will carry an additional component of variance due to this DH. If the rotary division involves taking 50 to 100 increments (50 to 100 revolutions of the divider) during the division process, the variance due to DH will have been reduced to a negligible magnitude.

4.3.1.3 Change in the sampling constant with material top size If the sub-sample is now crushed or pulverised to a new top size

D 2 from its

original top size D1 , the sampling constant for the material will be reduced. In order to understand how the value will change, the expression for the sampling constant must be examined in more detail. The intrinsic heterogeneity term is separated out thus:

⎛ aij − aL ⎞ IH i = ∑ Yij ρ ij ⎜ ⎟ j =1 ⎝ aL ⎠ Nc

2

[Eqn 4.22]

This term is equivalent to a variance of the composition distribution over the composition classes. In fact, if the density of the particles does not change significantly from one composition class to the next (the phase carrying the measurand does not differ in density from the remainder of the particle or is present in low concentration) and the mass of material in each composition class is constant, then:

Yρ IH i = i 2 i aL and

IHi

Nc

∑ ( aij − aL )

2

[Eqn 4.23]

j =1

the intrinsic heterogeneity of the I

th

size fraction is directly

proportional to the variance of the composition distribution. In general , when an ore or similar material is further comminuted, there is a tendency for mineral phases to liberate and the concentration distribution becomes wider. In extreme cases, when all particles in a size fraction consist

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of one or another pure mineral phase, the heterogeneity of the size fraction cannot increase further. The sampling constant is now written as: NS

Ks = ∑ X iVi IHi i =1

[Eqn 4.24]

It should be noted that the intrinsic heterogeneity (IH) of each size fraction is multiplied by the volume of the particles in the size fraction. For many materials, this means that the value of the sampling constant is controlled by the IH of the coarsest particles in the material; while the fines may show a substantially wider composition distribution, their influence on the value of the sampling constant is suppressed by the particle volume factor. If the shape of the size distribution remains more or less similar as the material is comminuted (the cumulative size distribution is simply shifted sideways on a log size plot), the sampling constant will in most cases (there are important exceptions) be proportional to the cube of the top size of the material. Reducing the top size by a factor of 2 reduces the value of the sampling constant by a factor of 8, if the phases carrying the measurand are not significantly liberated by the comminution. If the liberation does increase with reduction in top size, then the sampling constant is reduced by a factor less than 8. When dealing with complex materials, where the measurand(s) are carried in a number of mineralogical phases, it is very difficult to predict the real extent of reduction in the sampling constant with reduction in top size. This is especially true when the starting material is coarse and there is little liberation of the phases carrying the measurand. If the material is crushed past some critical size, which will depend on the nature of the material, the intrinsic heterogeneity of the coarser size fractions may show a substantial increase, possibly by as much as an order of magnitude. In such a case, the sampling constant reduction with size would be seriously reduced. There is potentially, therefore, a substantial risk associated with making assumptions and extrapolations of the value of the sampling constant over the particle size scale as proposed by the use of the Sampling Safety Line (Gy 1979).

4.3.1.4 The value of the sampling constant for the analytical aliquot When materials are pulverised prior to weighing out an analytical aliquot, the material is usually visibly very fine. This often leads to the assumption that the material is so fine that the intrinsic heterogeneity variance component associated with the mass reduction from the analytical sample of mass to the analytical aliquot mass

M aa

is negligible.

M as

In many cases, this

assumption is untrue. Low-grade materials in particular can be shown to AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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have significant intrinsic heterogeneity components associated with this step. If the material cannot be successfully further reduced in particle size to reduce the effect, it is necessary to fuse the material to destroy its heterogeneity, to pulverise it again and then to extract the aliquot. If fusion is not an option, and the variance of the analysis is to be reduced, the aliquot mass must be increased. Analysis of materials by XRF using pressed pellets must be approached with particular care when low atomic number elements are the measurand. While the mass of the aliquot used to make the pellet may be substantial, the analysis depth of the x-rays from the element in the pellet material can be very low (10s of microns: a Cr x-ray in chromite loses 90% of its intensity over 60 microns). The effective aliquot mass is then controlled by the analysis spot size and the x-ray analysis depth. The effective aliquot mass may be reduced by a factor of 1000, increasing the intrinsic heterogeneity standard deviation due to aliquot mass by a factor of about 30.

4.3.1.5 Determination of the value of the sampling constant for a material Use of the basic formula:

⎛ aij − aL ⎞ Ks = ∑ X iVi ∑Yij ρij ⎜ ⎟ a i =1 j =1 L ⎝ ⎠ Ns

Nc

2

[Eqn 4.17]

for the calculation of the sampling constant presupposes that the composition distribution with respect to the measurand for all the size fractions that have an impact on the sampling constant value can be determined. In many cases carrying out such an analysis is simply not a realistic goal; some other means must be found for estimation of the value of

Ks .

There are three basic methods that can be used:

•

Individual particle analysis, also known as the ‘50 (or more) piece analysis’ method

•

Use of scanning electron microscope data from particle sections

•

Multiple sample analysis

These methods all have their limitations, which are discussed below. Individual particle analysis If a material has a top size that makes it feasible to analyse individual particles for a range of measurands, the measurand(s) is present in reasonable concentration, and the material shows significant liberation of the phases carrying the measurand(s), this method can be very effective. A specially collected composite sample that is deemed to be similar in heterogeneity to the average material to be sampled is first divided to provide AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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a sample for size by size analysis; this sample is screened on at least a √2series of sieves and each size fraction is prepared for analysis with respect to the measurands of interest. From a suitable sample or sub-sample of the remainder of the material, the two top size fractions are screened out. These sieve fractions should be chosen so that they each carry about 10% by mass of the size distribution and the actual mass fractions that they represent should be calculated and recorded. These fractions do not have to coincide with the fractions defined in the first sample, but it is convenient if they do. Then, each fragment is numbered and 50 fragments from each size fraction are randomly selected (by choosing the 100 numbers at random). Each fragment must be carefully prepared and analysed to produce 100 results for each measurand of interest and the standard deviation associated with the analytical methods must be known. For the coarsest material, a histogram of the results for each measurand is then plotted. To apply the 50-piece method to the estimation of the sampling constant for a chosen measurand, the histogram should show a spread (standard deviation) that is significantly larger than the standard deviation associated with the analysis. Furthermore, the spread of the analyses of the full set of size fractions should not be substantially larger than the spread for the particles in the top size fraction. If there is evidence in the finer fractions of unusual liberation of phases carrying the measurand(s), one must proceed with more caution (this issue is illustrated below). The sampling constant can be written as:

K

s

∑

=

i =1

ie , K ∴ K

Ns

s

s

=

⎛ a ij − a L X i ∑ Y ij V i ρ ij ⎜⎜ aL j =1 ⎝ Nc

Ns

∑

i =1

=

2

∑

i =1

⎛ a ij − a L X i ∑ Y ij m ij ⎜⎜ aL j =1 ⎝ Nc

⎛ a ij − a L X i ∑ Y ij m ij ⎜⎜ aL j =1 ⎝ 50

2

⎞ ⎟⎟ ⎠

⎞ ⎟⎟ ⎠ ⎞ ⎟⎟ ⎠

2

2

[Eqn 4.25] Assuming that the 50 particles in each of the two top size fractions are a sufficiently good representation of the material, the mass fraction of the jth particle in the size fraction is:

Yij =

mij 50

∑m j =1

ij

[Eqn 4.26]

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⎛ aij − aL ⎞ ⎟⎟ m ⎜⎜ ∑ 2 a j =1 ⎝ L ⎠ Ks = ∑ X i 50 i =1 ∑mik 50

2

2 ij

[Eqn 4.27]

k =1

The values of

X1

and

X2

are now the mass fractions determined by

screening out the fractions from the sample (the aim is to have about 10% in each size fraction). Note that the analysis

aL in

the above formula is the average measurand

concentration determined from the size-by-size analysis of all the size fractions and the

a ij

are the analyses of the individual particles.

The use of this formula is illustrated in Appendix F. An example of a material where this method must be applied with caution is provided by the UG2 ore of South Africa. Figure 4.2 below shows the grade of the particles in a run of mine sample crushed to a nominal top size of 3 mm, on a size-by-size basis. The UG2 ore is a chromite cumulative texture in which the sulphides and PGMs are found in the interstices of the texture dominated by euhedral chromite grains. The larger material has a grade that is essentially equal to the head grade of the ore. Chromite grains dominate the size fractions near 350 microns and these fractions are relatively low in PGMs (the PGM grade goes down to 0.5 g/t). The heterogeneity with respect to PGMs of the fractions above 350 microns is low, but for sizes below 100 microns, the heterogeneity increases radically. Given the particle volume weighting, the overall heterogeneity of the ore with respect to PGMs will have contributions from all but the finest fractions. The heterogeneity with respect to chromite will be dominated by the larger particles because their grade differs significantly from the head grade. This ore provides an example of a material that must be carefully analysed to discover its full heterogeneity with respect to all measurands of interest.

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60

25

50

20

40

15

Cr 2 O 3 head grade

10 5

Pt head grade

0 1

10

100

1000

10000

30 20

Cr 2O 3 [%]

Pt [g/t]

30

10 0 100000

Particle Size (microns)

Figure 4.2: The grade of particles in a run of mine sample of UG2 ore crushed to a nominal top size of 3 mm, on a size-by-size basis

Use of scanning electron microscope data When the particles in the material are too small for individual analysis, it is possible to take advantage of the automated electron microscope instruments, QEM*SEM and the Mineral Liberation Analyser (MLA) that have been purpose built to analyse sectioned particle mounts rapidly and effectively. These instruments are specialised to scan sectioned and polished mounts of particles of an ore; they have the facility to identify mineral phases and to analyse by XRF the phases with respect to a given element. They also have the facility to search the mounts for mineral grains of rare phases such as gold, electrum and PGMs. If a chosen size fraction of particles is mounted in resin, cut, polished and prepared for an electron microscope, the instrument will provide a set of analyses of hundreds or thousands of individual particle sections. The instrument will provide the area fraction of each mineral for each particle section. The areal fraction of the particle section does not unequivocally provide the volume fraction of the mineral in the particle. The difference between the two is called stereological error. In some cases stereological error is negligible and in other cases it can be significant. Procedures for correcting this error are available but seldom applied in practice. Such procedures are called stereological correction. This means that if, instead of using stereological correction, the distribution of the areal fractions is used as a surrogate for the volume fractions of the target mineral phase, the standard deviation of the distribution is always overestimated. However, the mean of the distribution is unbiased, which means that assays estimated from area sections give an unbiased estimate of the assays of the particles.

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If the two top size fractions of, for example, a flotation concentrate are mounted and analysed, the sampling constant for the material can be estimated, under the same caveats as for the ‘50 piece analysis, from this electron microscope analysis. It is important to know the average measurand concentration, aL for the sample, and it is necessary to convert from volume to mass fraction units of concentration. Thus, the calculations for the method are rather more involved than the procedure for large particles. However, this method permits the analysis of particles down to some 10s of microns in size and so can reveal heterogeneity problems with the analytical aliquot. Multiple sample analysis When the above methods cannot be used, it is necessary to resort to the multiple sample analysis method. This method is very simple, but it is necessary to be aware of the impact of analytical precision of the values used in the calculations relating to the method. This method can be used to track the material heterogeneity as a function of the material top size. The concept of the method is directly related to the form of Equation 4.17, which indicates that the variance of measurand concentration between nominally identical sub-samples of mass M s is inversely proportional to the sample mass. To establish the sampling constant at a given top size of the material of interest, a set (six or more) of nominally identical samples of a series of nominal sample masses is prepared by rotary sample division. The samples masses are denoted as Ms1 , M s 2 , …, Msn. Each of the six or more samples of a given nominal mass is analysed for the measurands of interest and the variance of each of the measurand concentrations over the set is calculated in the usual manner. The process is repeated for the progressively smaller nominal masses,

Ms1 , Ms2 , …, Msn to provide variance estimates sˆ12 , sˆ22 , …,

sˆn2 . Then a plot of the variance versus the reciprocal of the sample mass is made and a regression line fitted to the points that follow a linear trend. The slope of the line is the value of the sampling constant at the particular top size. This method must, however, be used with common sense. When the sample mass is large, the measurand variance over the nominally identical samples will indicate nothing but the analytical variance. The variance over the samples will not increase until the mass is low enough to expose the material heterogeneity. The heterogeneity with respect to different measurands may well appear on different scales.

4.4

ACCURACY OF THE SAMPLING SYSTEM

A sampling system that is mechanically correct cannot be biased in the long run. The most effective manner of ensuring that sampling systems are free of bias is to design them to be mechanically correct under all circumstances (material types and AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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range of mass flow). If a sampling system cannot be designed, installed, operated and maintained in a manner that is unequivocally mechanically correct, provision must be made to allow accurate bias testing. If the nature of the flow being sampled changes, a sampler that is not mechanically correct must be bias tested on the new material type. A correct sampling system can show a bias over a short period of time if the sampler takes increments at fixed time intervals, and this interval is a multiple of the period of a periodic variation in the mass flow or quality of the process stream. To eliminate this effect, the sampler must be operated in a random stratified sampling mode, preferably based on time intervals. Stopped belt sampling using a correctly designed former, which is the recognised reference sampling method for conveyer systems, can obviously be used. But there is usually a significant cost associated with a stopped belt exercise and this procedure is usually reserved for establishing the accuracy of alternative sampling methods. The sampling from stockpiles and bins for purposes of Metal ing must be avoided because it is almost impossible to obtain a representative sample. The recommended method is to evaluate the material as it is added to or removed from the stockpile or bin. The sampling of trucks, wagons, skips and drums must only be undertaken using correctly designed auger samplers and in accordance with a Standard such as JIS M 8100. Solutions, but not slurries, in pipes must be sampled after ing through an efficient in-line mixer, and in drums they must be thoroughly stirred before sampling. If there are suspended solids in the drums, the contents must be filtered and the solution and solids sampled and analysed separately by accepted procedures. Final metals must be sampled according to the appropriate standard in order to obtain an unbiased sample with respect to the trace elements. Effluents and discharges through stacks normally only have a minor effect on Metal ing, although this is not the case if ability of sulphur in a smelter, for example, is important. Accurate stack sampling requires specialised equipment and expertise and for this reason, it is often outsourced. Effluents can be accurately sampled using cross-stream or flexible hose samplers. The requirements for an accurate cross-stream sampler are given in a number of texts and they include the following list given by Holmes (2004):

•

The sample cutter must be non-restrictive and self-cleaning

•

The geometry of the cutter opening must be such that the cutting time at each point in the stream is equal. For linear-path cutters, the cutter edges (called lips) must be parallel and for cutters travelling in an arc or a circle, they must be radial. Flap or diverter type cutters are always incorrect in this regard, because they divert one side of the stream for a longer period than the other (Pitard)

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•

No materials other than the sample must be introduced into the cutter e.g. dust or slurry must be prevented from accumulating in the cutter when it is in the parked position

•

The cutter must intersect the stream in a plane normal to, or along an arc normal to, the mean trajectory of the stream

•

The cutter must travel through the stream at a uniform speed – an adequately powered electric drive is a pre-requisite for this

•

The cutter aperture (gap) must be not less than three times the nominal top size of the material, with a minimum size of 10 mm. For wet solids a minimum of 50 mm is recommended to avoid bridging

•

The cutter must have sufficient capacity to accommodate the increment mass at the maximum flow rate of the stream. Bucket samplers, in particular, must have sufficient capacity to accommodate the increment mass at maximum flow rate of the stream without any reflux or overflow of material from the cutter aperture

•

For slurries, loss of sample material due to dribbles must be avoided

•

The maximum cutter speed must not exceed 0.6 m.s-1

•

Cutters that collect only part of the stream on a continuous or periodic basis e.g. “shark fin” cutters, pressure pipe samplers, T-pieces or “bleeds” from the side of a pipe must be avoided

•

For sticky materials, steep chute angles of >60o and stainless steel or polythene chute linings are recommended to reduce adhesion, and the cutter gap may also need to be enlarged to prevent bridging

•

The plane of the cutter lips must not be vertical or near vertical, because particles that strike the inside edge of the cutter lips are deflected away from the cutter by gravity

•

If a belt scraper is required to remove material adhering to the belt, the scraped material must fall within the area traversed by the cutter

Another very useful source of information on factors to be considered in the design of accurate samplers is the paper by Pitard presented at the Second World Conference on Sampling and Blending, held in Brisbane in May 2005.

4.4.1 Reference Sampling Method The accepted Reference Sampling Method for material on conveyors is the so-called Stopped Belt sampling method. Here the material of interest is moved on a conveyor belt, which is stopped periodically. A former that is as wide as the belt, with a separation of the plates of not less than three times the nominal top size of the material with a minimum size of 30 mm, is placed on the belt and all of the material between the plates is removed. In the situation where the position of the former is fixed, it is the convention that any material caught under the plate on the upside of the conveyor is included in the sample and that on the downside rejected. An effort should be made to ensure that the mechanical sampler being tested takes its increment from material as close to that taken by the stopped belt former as possible. The absence of bias from the mechanical sampler is established by conducting the AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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experiments specified in a number of Standards such as ISO 13292:1997, ISO 3085:1998, AS4433 and JISM8100. The data analysis method must consider both bias of scale and absolute bias.

4.4.2 Slurry Sampling Obviously, the stopped belt sampling method cannot be used as a reference method for slurry being conveyed in a pipe or launder. In this case it has been stated by Gy (1979) that, provided the cross-stream sampler has been installed to satisfy all of the points given in Section 4.4, the sample may be considered to be accurate. It would also be prudent to add the points given by Bartlett (2001) concerning the design of the launder, the slurry velocity, a cutter gap of 20 mm, a cutter speed of 0.3 m.s-1 and the streamlined design of the cutter body. As pointed out by Hunt (2002), however, the fact that a sampler obeys all of the rules for a correct design does not automatically produce an unbiased sample. Great emphasis must be placed on the ongoing correct operation of the sampler, sample presentation and the auditing of the system.

4.4.3 Stock-pile or Bin Sampling Unless the material in a stockpile or bin is completely homogeneous, it is impossible to sample accurately and, therefore, attempts to include such samples in a metal ing process will invariably lead to inaccuracies. The only measurement of a stockpile or bin that can be performed accurately is that of volume. But the conversion of this measurement to a dry mass is confounded by the fact that it is almost impossible to obtain accurate bulk density and moisture values. It is recommended that the contents of stockpiles and bins only be evaluated by the accurate sampling of material as it is added to or removed from the stockpile or bin. If this is impossible and there is no viable alternative to attempting to sample a stockpile, then the situation must be covered by an Exception Report.

4.4.4 Truck, Wagon, Skip and Drum Sampling It is possible to establish the accuracy of auger sampling of trucks or wagons if the material can be transported by a conveyor and subjected to stopped belt sampling (Section 4.4.1) or re-slurried and sampled by a correctly designed cross-stream mechanical sampler (Section 4.4.2). For an auger to have a reasonable chance of being accurate, the particle size of the material being sampled must be reasonably fine and there must be no free water associated with the material being sampled. In addition, the auger must penetrate to the bottom of the “container” and all of the material sampled must be retained within the auger. A spear sampler, i.e. a tube sampler without an inner Archimedean screw, is not recommended even if it is mechanically driven. This is because after a certain distance, the frictional force due to the material going up the inside of the spear becomes greater than the force required to move aside the material on the outside of the tube. Consequently, only the upper layer of the material will be sampled. The precision of an auger sampler can also be established by carrying out a suitable number (>30) of inter-leaved sampling experiments on the material being sampled.

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A paired t-test can then be applied to the data sets to confirm that there is no significant difference between the pairs of results.

4.4.5 Solution Sampling The accuracy of solution sampling can be established by experiments that compare the concentration of analytes in the sample with the concentrations in the reject solution. This is usually achieved by conducting experiments where a solution is pumped past the same sampling point at least 10 times and the analytical data from the successive samples is examined statistically to ensure that it is free from drift. Because solutions pumped through pipes can be thoroughly mixed using in-line mixers, the normal requirement, of repeated sampling of all of the stream part of the time, can be relaxed and a portion of the stream can be removed under controlled conditions. There are a number of commercially available sampling systems that can do this. However, there are examples, for instance in a precious metals refinery, where the density of a process liquor can be so high that segregation of the liquor occurs. In these cases it is impossible to take a dip sample and the rule of sampling a process stream while it is in motion must be applied. There are areas in Hydrometallurgical Plants where solutions must be sampled when they are hot and/or under high pressure. In these circumstances, specialised equipment is usually necessary and care should be taken to ensure the safety of the plant operators. In addition, care must be taken to avoid the evaporation of water. The effect of crystallisation when the solution cools to ambient temperature must also be taken into .

4.4.6 Final Metals and alloys Because of the normally high purity of final metals, >99%, they are essentially homogeneous with respect to the major constituent and, therefore, the sampling of them to for the major metal is not problematic. It is usual to establish the concentration of the major metal by summing the concentrations of the impurities and subtracting this value from 100%. Sampling for the evaluation of trace impurities, however, should be carried out according to an appropriate standard such as ASTM1587. The sampling of ferro- and non-ferrous alloys is well covered in a number of international standards (Appendix C.2.3).

4.4.7 Scraps and residues Frequently the sampling of scraps and residues has particular difficulties. This is because they can consist of a mixture of metallics and plastics, e.g. computer scrap, or other materials. The design of a sampling protocol for scraps and residues must, therefore, be done with a high degree of common sense and with due regard to the requirements for obtaining an accurate sample. For example:

•

Computer scrap may be chopped into fine pieces prior to sampling

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•

4.5

Residues containing metallics and powder could be sieved to separate the two and each material sampled separately

PRECISION OF THE SAMPLING SYSTEM

The precision of a sampling and sample preparation system is determined unequivocally by inter-leaved sampling methods, the procedure for which can be found in a variety of Standards (e.g. ISO 12743 and ISO 12744:1997). In an interleaved sampling test, the primary sample cutter is operated at twice the usual rate and the alternate increments are directed to separate containers. If the sampling system consists of more than one stage, the material from successive primary increments must remain separate as it moves through the sampling system and is collected in the separate containers. The two sample containers are collected at the end of the sampling period and they are processed separately to arrive at a set of analytical results. By collecting a substantial number of samples in this way, the average absolute difference between the sample pairs, or the mean squared difference between the sample pairs, can be used to estimate the total variance of sampling, plus sample preparation, plus analysis. If it is an objective to break this variance down into its components, additional work must be undertaken to establish the analytical variance (which should be known from records of duplicate determinations in the laboratories) and the sample preparation variance. The Standards are very clear as to how to undertake inter-leaved sampling work. However, if inter-leaved sampling is to be used periodically to establish the sampling variance, especially when changes in the process have been implemented, the system must be designed to allow sampling in this mode. The variance of sampling, with respect to a target analyte, is generally composed of two basic components when sampling a particulate material. The first, usually referred to as the 'fundamental error', is related to the range of analyte contents of the particle. It is possible to calculate a 'sampling constant', K s for a material in a given state of comminution and with respect to a particular measurand, (Section 4.3). If the mass retained at the end of a sampling period (e.g. a shift) is Ms , the sampling variance due to the fundamental error is:

σ 2 = a2

Ks Ms

[Eqn 4.28]

where a is the assay of the sample. Note that K s is usually calculated in units of grams. The second component of the sampling variance is due to the time variation of the grade of the process stream during the sampling period. This component of variance is proportional to the variance of the measurand concentration about the mean for the sampling period and it is inversely proportional to an increasing function of the number of increments taken from the process stream during the sampling period. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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In a multi-stage sampling system, it is vital that an adequate number of increments is extracted from each primary increment, and from all the successive stages of the system. This is particularly important in multi-stage slurry sampling systems. The number of increments required depends entirely on the tendency of the material to segregate with respect to measurand concentration or particle size, as it is handled within the sampling system. Ideally, 30 increments should be taken at each stage, but in practice, this is often difficult to achieve. However, especially in multi-stage slurry sampling, the extraction of numerous increments is important to reduce the possibility of bias in the sampling. It is in such a circumstance that Hunt's comment that a sampling system consisting entirely of correct samplers may still end up showing a bias is likely to occur. The bias incurred under these circumstances is always a consequence of allowing the material to be sampled to segregate. Thus, correct presentation of the material to be sampled to the sampler will essentially eliminate the bias. In a multi-stage sampling system, there is an increase in the sampling variance at each successive stage of sampling. If the sample is reduced in mass from Ms to Mss , the increase in variance is given by the following equation:

⎡ 1 1 ⎤ ∆σ 2 = a2 K s ⎢ ⎥ ⎣ Mss Ms ⎦

[Eqn 4.29]

where Ks is the sampling constant for the material at its particular state of comminution and a is the sample assay. If the sample is reduced in particle size by crushing or pulverising, the sampling constant will change in value. The change is determined by two competing factors. Firstly, the sampling constant is broadly proportional to the volume of a particle having a size equal to the 95% ing size, with the exceptions outlined in 4.3. Secondly, the sampling constant increases with the extent of the liberation of the phases carrying the measurand of interest. It must be emphasised that this latter variation is entirely dependent on the nature of the phases carrying the measurand of interest. A material can have a small sampling constant with respect to one measurand and a very large component with respect to a second measurand. For materials in which the phase carrying the measurand of interest is very high grade and the overall grade of the sample is low, the sampling constant may not behave according to the broad rules stated above. A coarse-grained low-grade gold ore may have an essentially constant sampling constant that does not depend on the particle size at all. A PGM material where most of the grade is due to 'metallics' will have a similarly perverse behaviour in of its sampling constant. It is therefore extremely risky to make broad statements regarding the nature of the fundamental error for an ore, with respect to any measurand. The sampling safety line plot, originally proposed by Gy, is based on the assumption that the sampling constant is proportional to particle top size cubed. If this is not true, the sampling safety line is, therefore, correspondingly misleading. Every stage of sample preparation in which the ore is reduced in size, and subsequently reduced in mass, causes a change in the sampling constant, and it AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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incurs an increase in the total sampling and preparation variance according to the formula above. This increase in variance continues right down to the point of extracting the analytical aliquot from the prepared sample. From the point of view of the variance of sampling, preparation and analysis, the preparation stages must be treated in exactly the same way as the sampling stages. When the material being sampled changes in its mineralogical make-up, or the intensity of the process grade variations with respect to any measurand of interest changes, the precision of sampling may need to be re-estimated. New tests of the sampling precision, using inter-leaved sampling, may be the most effective way of determining the change in precision. Given that the sampling systems have been designed to permit inter-leaved sampling, which is highly recommended for metal ing samples, this is a simple procedure which can be carried out periodically and documented on an on-going basis. Such a procedure is a sound component of a quality assurance program. It is recommended that the precision of samplers be measured on a regular basis, at least annually. This allows the contribution of the samplers to the total variance to be calculated, thereby indicating where effort should be focused in the valuation system.

4.6

MULTI-STAGE SAMPLING SYSTEMS

It is quite probable, due to the constraints on the requirements of the sampling device e.g. cutter gap, cutter speed and number of increments, that the mass of sample, especially if taken at primary or secondary sampling stages, will be too large to submit to a laboratory for analysis. This is most likely to be true for coarser materials and/or process streams with a large tonnage throughput. In these cases, it is usual to design a sampling system with multiple stages. It is important that any reduction of sample mass is done with the objective of minimising additional sampling variance. Three methods can be used to establish the Sampling Constant and hence the sampling protocol to achieve a desired level of sampling variance and they are discussed in Section 4.1. In addition, to enable the recommended number of increments (>30) to be taken at each sampling stage, intermediate storage bins with controlled feed to the next sampling/reduction stage are normally required. Secondary and any subsequent samplers must be running continuously or be triggered at a random time ahead of any material to be sampled. The bias of a sampling system can be determined only by comparison of the samples from the installed sampling system with some reference method of sampling (stopped belt for material flows on conveyors and some other method for other situations). Bias is not a statistical quantity and is, therefore, much more difficult to quantify than sampling precision. A test to estimate bias must be designed to detect a certain level of bias. As the magnitude of the bias to be detected becomes smaller, in relation to the sampling precision (or standard deviation), the number of pairs of 'test' and AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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'reference' samples needed to show up the bias in a statistically significant manner becomes steadily larger. To find a bias that is one quarter of the standard deviation is very difficult. Standards deal very effectively with the estimation of bias and the general rules for doing the tests are all similar. The statistical analysis of the test for bias must include a test for bias of scale as well as for absolute bias. The method by which this can be done is documented in an article by Lyman (1993). The best defence against bias in sampling systems is the mechanically correct sampling of the process stream and the extraction of adequate numbers of increments at each stage of sampling. The extraction of the analytical aliquot from the mass of prepared sample must not be ignored. This is almost universally done by use of a spatula, which is not a mechanically correct method of sampling and it is prone to bias. Where the material for analysis has refractory and easily ground phases and density differences between mineralogical phases, there is a perfect opportunity for phase segregation at the very point where the phases are best liberated. To reduce bias and variance to their minima, miniature rotary sample dividers should be used to extract the analytical aliquot from a mass of sample that has been prepared to a target mass without any manual 'dipping' of sample increments. To accomplish this task, a 'variable split' rotary sample divider is recommended. In the Coal Industry, for example, the particle size of a plant product can be an important measurement parameter. Particle size reduction, therefore, cannot be carried out during a multi-stage sampling system on the portion of the sample to be used for particle size measurement. Systems have been designed, however, where a separate portion is taken specifically for particle size analysis.

4.7

SAMPLE STORAGE PRIOR TO DELIVERY TO A LABORATORY

Prior to their delivery to a laboratory for analysis, it is important that samples are stored in suitable sealed containers in areas where any change to their physical or chemical parameters is minimised. This is especially important with respect to their moisture content.

4.8

SAMPLE LABELLING

All samples must be labelled in such a way that their unique identity is unambiguous. There are several computer-based sample-numbering protocols available to ensure this unique identity. The use of a bar coding system is preferable; however, to maintain readability, small bar coding systems should be avoided

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5 5.1

SAMPLE PREPARATION AND ANALYSIS

INTRODUCTION

All operations in sample preparation and analysis must be carried out in order to provide accurate data with a known and acceptable precision within a time frame that is suitable for Metal ing purposes. Cross-contamination of samples during sample preparation must always be guarded against. Samples with known differences in concentration such as feeds, tails, concentrates, mattes and slags must be prepared in separately enclosed areas using dedicated equipment. In cases such as underground and geological samples, where large variations of grade are unavoidable and unknown, it must be established that there is minimal hold-up of material within the sample reduction equipment. Frequent cleaning of this equipment may be necessary.

5.2

SAMPLE PREPARATION

5.2.1 Moisture Determination The moisture determination must be carried out as soon as possible after the sample has been taken and the sample must have been stored in a sealed, air-tight, noncorroding container. The whole sample received in the Laboratory must be dried to constant mass for the moisture determination. When removing the sample from the canister in which it arrives at the laboratory, specific attention should be given to removing moisture in the container that has 'sweated' out of the sample. A tared piece of paper towel can be used for this task. This issue is clearly more important for samples of relatively small mass. In some operations where a large sample is delivered to the laboratory (>10 kg) this must be sub-sampled through a correctly designed sample division system, in an enclosed space (particularly in conditions of low ambient humidity), to provide material for further treatment within the laboratory. The drying temperature, which must not exceed 105°C, must be such that the physical and chemical properties of the sample are unaffected. In the Coal Industry, for example, it is usual practice to air-dry samples at ambient temperatures.

5.2.2 Sample Reduction Sample reduction must only be carried out in equipment that does not contaminate the sample. Because “zero contamination” is probably not achievable, experiments must be conducted to establish the level of any contamination by running suitable blank samples through the system at periodic intervals. In the cases of known potential contaminants, such as cobalt from a tungsten carbide mill and chromium from a ferrochrome mill, alternative systems should be chosen if the contaminant is a required measurand. Dust losses must be avoided because there have been numerous occasions where it has been demonstrated that the fine material in a sample has a very different AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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concentration range than the bulk material, for certain measurands. Examples of this are ROM samples in the Gold Mining industry where, due to density differences, fines likely to be lost as dust essentially have a zero gold content, and in Platinum smelters, fines in converter matte samples have very low PGM concentrations. In both cases, the loss of fines during the sample preparation process will, therefore, significantly upgrade the sample. Suitably sized jaw and roll crushers, pulverisers and “puck and ring” mills are acceptable for sample particle size reduction. Sample reduction ahead of sample division must always be done to a particle size that is known to maintain the sample preparation variance at the desired level for the sample concerned, as determined by an appropriate test and analysis of sample variance.

5.2.3 Sample Mixing Provided that Sample Division is carried out using correctly designed mechanical spinning riffle systems, samples do not require further mixing after a reduction stage. If the sample is to be mixed, sealed mechanical systems such as Y-blenders or other suitable mixers are preferred as they prevent dust losses during mixing. Care must be taken to ensure that the mixing containers are not filled to more than 50% of their volumetric capacity. Paper or sheet rolling, which is sometimes used in sample preparation facilities, must be avoided, because of the potential for dust loss, increased segregation and inadvertent sample loss if the mixing is carried out too vigorously. The mixing tray procedure specified in JIS M 8100 and by Merks (1985) is acceptable, where sealed mechanical systems are not available. Because this is a manual system, there is an upper limit to the sample mass that can be handled in practice.

5.2.4 Sample Division The degree of sample division must always be done with the aim of achieving a desired level of sample preparation variance. Properly designed mechanical sample division systems, such as rotary rifflers, are preferred provided that the requirements of good sampling practice such as speed of rotation < 0,6ms-1, radial cutter gaps and a minimum of 30 increments are observed. In addition these systems should provide completely proportional samples and QC procedures must be in place to confirm this. The Incremental Division Procedure specified in JIS M 8100 and Merks (1985) is acceptable. Stationary rifflers must be avoided, because, even when the riffler is equipped with a feed bin to ensure the even distribution of material to both sides of the divider, the probability of unsatisfactory operation is too high.

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5.2.5 Sample Storage after Sample Preparation Samples, including acid liquids, must be stored in sealed glass containers, after sample preparation, to minimise any possible physical or chemical changes. Alkaline liquid samples must be stored in sealed plastic containers. If samples are to be stored for long periods, i.e. for longer than 3 months, they must be stored under nitrogen. For periods longer than 6 months, consideration should be given to storage in hermetically sealed plastic containers under nitrogen. After prolonged storage, solid samples must be mixed in a sealed system prior to extracting an analytical portion. There must be an appropriate istration system in place to monitor the removal of samples from storage for the purposes of further analysis or disposal.

5.3

ANALYSIS

5.3.1 Laboratory Accreditation Analytical procedures used to analyse samples for Metal ing must be accredited to the ISO 17025 standard. This has become the de facto worldwide standard for laboratory accreditation, with local accreditation boards established in most countries with significant mining/metallurgical operations. Accreditation is seen as a requirement for material such as concentrates and mattes that are part of a commercial transaction between a buyer and seller and for other materials that are part of a metal ing system. It is likely that most of the procedures in the following sections will be implemented as part of an accreditation exercise.

5.3.2 Twin Stream (independent parallel) Analysis Samples for Metal ing should be analysed in a twin stream analysis scheme in which a set of nominally identical aliquots of the sample are analysed by different analysts using different instruments at different times. There should be no more than 6, but at least 2 nominally identical samples in each set. A maximum of 6 samples is recommended for each set because fewer samples are inefficient and it has been found that if the number becomes too large there is a tendency to avoid strict application of any QC procedure.

5.3.3 Reference Materials In addition to the samples, each set of materials analysed should include at least one Reference Material (RM), which is similar in matrix and concentration to the samples. Preferably this should be a Certified Reference Material (CRM), if it is an Internal Working Reference Material (IWRM) then the accepted values should have been derived by a multi-laboratory round robin exercise using a CRM as a QC sample. Periodically a Reference Material should be included in routine analysis, preferably as a blind sample; it is common practise for blind samples to constitute 5–10% of a laboratory’s throughput.

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5.3.4 Instrumental Calibration (I, AA, UV/VIS, XRF [solutions]) At least seven standard solutions, including a blank, should be prepared to cover evenly the calibration range, which itself must be designed so that the concentrations of the sample solutions are close to the centre of the range. The standards should preferably be matrix-matched to the sample solutions which should be measured in a random order, both before and after the sample solutions. The best practice of instrumental calibration is covered in the VAM document LGC/VAM/2003/032.

5.3.5 Instrumental Calibrations (XRF [Pressed Powder and Fused

Bead]) At least 15 standards are required to cover the expected concentration range of the samples and, in the case of the Pressed Powder technique, they should be closely matrix-matched to the samples to be analysed. This will probably require that the standards are replaced on at least an annual basis. The analytical values assigned to the standards must be accurately established using other accredited methods. This can be done in-house or by using the facilities of another laboratory that has demonstrated its ongoing ability to analyse the types of sample concerned. The concentrations of all non-metal ing elements with a concentration > 2% must have accurate values assigned to them to allow for the estimation of inter-element effects. The sum of all known elements in the standards must be > 95%. The best practice for calibrating an XRF instrument should be followed. Particular attention should be given to ensuring that all pressed pellet standards have the correct particle size distribution.

5.3.6 Instrumental Calibrations (Other) Calibration of other analytical instruments should always be to the best practice possible. This is in relation to the number of standards required to establish the response of the instrument, full coverage of the analytical range, matrix matching to the samples and measurement of the precision of the instrument during the analysis of a batch of samples.

5.3.7 On-Line Analysers A specialised area of instrumental analysis is On-Line Analysers, but they are not often used for Metal ing purposes; however, but where they are used, their calibration should be carried out to satisfy the requirements of Section 5.3.6.

5.3.8 Fire assay Fire Assay is used extensively in the Gold and PGM mining industries and special care must be taken to ensure the reproducibility of analytical conditions such as fusion and cupellation temperatures and times. QC samples must be analysed with each set of samples with its replicates distributed amongst the sample positions. Because of the known losses during the lead collection fire assay of the PGMs, which are reasonably reproducible if the operating conditions are kept constant, a fire assay correction factor is often used to correct for these losses. These corrected values must not be used for Primary Metal ing, which must be based on a technique such as nickel sulphide collection, but they may be used for Secondary AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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ing and Plant Control purposes. The same stipulation applies to gold assays where a silver correction has been applied.

5.3.9 Mineralogical Analysis In some operations there is a requirement to analyse samples for their mineral rather than their elemental content. Preferably this analysis should be carried out using computer controlled electron microscopes, but if it is done manually by point counting, then for major (> 50%) phases/minerals at least 600 points must be counted and for minor (± 5%) phases/minerals, the point count must be increased to 6 000. The number of points must be sufficient to provide a statistically meaningful result, and will depend on the variability of the material and the required precision.

5.3.10 QC Procedures QC procedures must be established, and strictly adhered to, in relation to the allowable differences between the accepted and actual values of the Reference Materials and the differences between replicates in the twin stream (independent parallel) sets. The use of Shewhart and CUSUM control graphs are recommended for the monitoring and rapid identification of possible analytical problems. Action to be taken in these situations must be formalised in the QC procedures.

5.3.11 QA Procedures Regular multi-laboratory interchanges of samples (Round Robin exercises or Proficiency Testing) must be conducted with laboratories whose ability to conduct such analysis has been proven by means such as laboratory accreditation to ISO 17025. The exercise should preferably include laboratories that are independent of the organisation concerned. The statistical analysis of accumulated analytical results for reference materials and for duplicate determinations is a very valuable tool for the on-going improvement of analytical procedures and it is vital to the maintenance of targets for accuracy and precision within the analytical systems. These analyses also provide an index of the efficiency of the laboratories; an increase in the fraction of the samples that require repetition because they have fallen outside the tolerance for agreement between duplicates not only indicates that the precision of the analysis is decreasing, but it also increases the load on the laboratory, limiting its throughput of fresh samples. It is extremely important that all assay results are collected into a database. The assay duplicates that failed to meet the tolerance must be included in the database. If they are not included, the database becomes 'censored' and is therefore very difficult to use in statistical analysis, since distributions of assay results are then truncated.

5.3.12 Umpire Laboratory The availability of an Umpire Laboratory is an integral requirement of a laboratory carrying out analysis for Metal ing. The Umpire Laboratory must obviously be accredited to ISO 17025 for all analytical services that it provides. In areas where particular analytical skills are needed, such as PGM analysis, the Umpire Laboratory will be required to have on-going experience of the analysis and this will probably AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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result in the Metal ing laboratory having to submit samples regularly rather than on an as-required basis. The assay censoring issue also arises in the use of umpire laboratories. External laboratories may be asked to provide a database that indicates their ability to provide precise results. The results in this database must not be censored, since this would make it impossible to understand the risks associated with use of that laboratory.

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6

STOCK-TAKES

Physical stock-takes and the determination of the Plant Stocks and total Process Inventory are an integral part of metal ing. The calculation of the dry mass and content of process inventory held in stockpiles, bins, tanks or other process vessels is subject to a high degree of uncertainty, mainly due to the difficulty in obtaining a representative bulk density and sample as discussed in Section 3.5. It can also be difficult to make estimates of the volume of material that complies with the standard of accuracy needed for metallurgical ing.The effort put into the estimation of an item of plant stock should be dictated by the value of the material in that item, relative to the value of the entire stock, as well as by the accuracy of the balance being carried out. A great deal of reliance is placed by external financial auditors on “setting the clock to zero”; i.e. a full stock-take at regular intervals. The frequency with which stock-takes are carried out is a management decision and it should relate to the value of the plant throughput and the in-process inventories relative to the expected profit at year-end and the cost of executing the stock-takes, but it must be carried out on at least an annual basis. Stock-takes can be performed more frequently, for plant control, to a lesser degree of accuracy than that required for financial reporting. The latter stock-takes must be performed in conjunction with the company’s auditors. The cost of executing the stock-takes increases with the demand for the accuracy of the result. The final decision regarding stock-take frequency and accuracy will be determined by the level of financial risk associated with a particular strategy. The costs incurred should be offset against the benefit achieved by the more timely availability of accurate information of production performance and of stock shortages or excesses. The overall confidence level in the result of the stock-take must be established from the uncertainties in the component measurements and reported with the results. Having developed a stock-take policy, the procedure must be documented to ensure a clear definition of roles and responsibilities leading to the rapid and effective execution of the stock-take and the reporting of results. Procedures for handling differences between book and measured stocks must be fully documented. Any adjustments must be made only after the appropriate authorisation as defined in the Code and these Guidelines. It is readily demonstrated that in-process stocks and draw-down or build-up of stockpiles are likely to represent the largest uncertainties in a Metal Balance. The most successful strategy for elimination of these uncertainties is to use a policy of building parallel stockpiles, one of which can be exhausted at the end of the ing period. If stocks are never exhausted, measurement uncertainties will accumulate to a point where the balances will be essentially meaningless. Therefore, the policy of eliminating stockpiles on a regular basis must be built into the general processing strategy. In cases in which the in-process inventory has a value that is large compared to the value of the net input for the ing period, the ing strategy must involve

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an operating policy which permits the exhaustion of the in-process inventory immediately prior to the stock-take, such as the use of the Bubble Method.

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7 7.1

METAL BALANCING

INTRODUCTION

The metals/commodities, alloys or concentrates produced by a metallurgical processing operation are the source of income which both sustains the operation financially and justifies the initial capital investment. The income generated is the result of a commercial transaction, which is based on a mass measurement and one or more analyses, which indicate the mass or proportion of saleable materials and any penalty elements. As might be expected, products which do not achieve a target grade and/or contain penalty elements attract a lower price, as detailed in a contract of sale. A mineral or metallurgical processing operation, which “purchases” its feedstock, has similar quality and mass measurement issues relating to a transfer of custody for both feeds and products. Those operations which mine their own deposits as feed stock commonly have royalty obligations relating to the depletion of the resource and, in some cases, may attract environmental penalties if levels of, for example, acid generating material or heavy metals in water, exceed agreed limits. The metal balance is critical for corporate governance, for monitoring, checking and improving performance and for identifying and quantifying losses and emissions. A satisfactory metal balance provides a convincing demonstration that: "What comes into the operation, less any internal accumulation, plus any draw down from internal stock, is sufficiently similar, i.e. within acceptable limits in mass and analysed components, to what goes out as product(s) and reject." (Check in-Check out) This is the fundamental purpose of the metal balancing and reconciliation process. The calculation process will include calculations which that the data is as correct as expected and it will help to identify data which results from intermittent problems (systematic errors) as well as to identify biases which accumulate within the process flowsheet. The calculation process should provide the best possible estimates of product metal recovery, minor element and/or elemental distributions, ore source grades and tonnages and the accumulation of mass and metal values within the circuit. It should also identify significant biases in the data over time. In principle, smoothed data can be used, provided it is used on a consistent and transparent basis. Using the Check in-Check out System, all streams into and out of the process or plant, for which the balance is being performed, are measured, sampled and analysed. In of this Code, this is the standard procedure to be adopted for Metal ing in all operations and all deviations from this approach must be justified in an Exception Report and approved by company Management.

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Older plants, particularly those in which the metal ing system was not designed as a part of the initial plant specification, can experience difficulties in applying this system. Typical problems relate to the lack of suitable installed facilities for mass and flow measurement of all streams, resulting in incomplete ing data. Some operations, particularly those handling commodities such as coal or iron ore, do not normally measure the mass flow of every stream, nor is every stream sampled and analysed. In addition, there are certain types of operations or process flows where accurate measurement is difficult and/or expensive. Applying the Check in-Check out system will normally generate an imbalance, due to measurement, sampling and analytical errors, or unknown losses. This imbalance is treated as an uned gain or loss. The balance equation is: Output = Input ± Change in Stock and Inventory ± Uned

[Eqn 7.1]

The Uned is made up of: Uned = Unknown Losses ± Bias, Random and other Errors

[Eqn 7.2]

For each of the ing streams the precision of the dry mass, sampling and analytical measurements is calculated and the precision of the overall balance is then derived. If the imbalance or uned quantity is greater than the limits defined by this precision, or if the limits of the precision so calculated are excessive, then this should be regarded as material and the result of an unsatisfactory measurement or unknown loss. The manner in which this uned gain or loss is managed and investigated must be based on procedures laid down in of the Code. Beyond a transparent demonstration that "what goes in is statistically similar to what goes out" the metal balance allows key performance parameters or indicators (KPIs) to be calculated.

7.2

THE METAL ING PROCESS

Metal ing provides the interface between the metal recovery operations and the financial and commercial centres of the company. It involves the recording, to ing standards, of information relating to the flow and transfer of metals, while the term “to ing standards” includes criteria such as accuracy, integrity, security and auditability which must be met. In turn, auditability requires that all source data must be able to be traced and all changes to the data must be authorised and recorded, together with reasons for the change, and filed so as to provide an audit trail. There are usually legal requirements for the period over which metal ing documentation should be retained, typically for a minimum of fifteen years, although, ideally, it should be retained as a permanent record. Metal ing is a routine activity based on an ing period (typically one month), which is usually determined by company reporting requirements, but it could also be determined by specific reporting requirements for the operation concerned.

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Primary ing has been defined as the balance across an entire plant and it is the major focus of this Code. Often, because of process time lags and process inventory, meaningful calculations can only be made over a longer period e.g. a quarter. Provided the ing period selected is relatively large, compared with the process residence time, a mass balance can be carried out using all available data. If this mass balance indicates a large uned balance, there may be problems in the mass measurement or sampling and analysis techniques. The source of these problems may be isolated by conducting mass balances over smaller sections of the total plant circuit, defined as Secondary ing, which is used to identify where inventory build-up is occurring, time lags are involved or where any measurement problems exist, and/or by the various techniques discussed in Section 7.4 and in Appendix E. These techniques are utilised to reconcile the balance in order to assess bias and other errors, or unknown losses, as well as to identify where effort should be concentrated to improve mass measurement, sampling and analysis. The Metal ing Process can be considered as a flow of measurement activity and Custody Transfers, through the various stages of processing from ore resource to customer. Thus, the metal balancing process will often cover several different plants. Some typical examples of the processes and the measurement basis commonly used for these processes are shown in Table 7.1 below. Table 7.1: Typical Stages of Metallurgical Processing Area

Representation/Process

Measurement Basis

Mine

Mine Reserves or Resource Model

Drill Core/Geostat Model

Mining Process

Number of Trucks/Hoppers Truck/Hopper Factor Block Model Assays Survey Measurements Bulk Density and Moisture

Custody transfer to concentrator

Concentrator Feed Stockpile

Occasional Surveys

Comminution and Separation To Rejects (Tailings)

Flow Rates, Density, Mass/Flow Measurement, Samples for Moisture and Analysis

To Products (Concentrates)

Flow Rates, Density, Mass/Flow Measurement, Samples for Analysis and Moisture

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Area

Representation/Process

Measurement Basis

Custody Transfer to Transport

Concentrate Load out/Shipment

Certified Masses,

Custody Transfer to Smelter

Concentrate Receipt

Certified Masses,

Transfer to Transport Samples for Moisture and Analysis, Sizing, TML, etc Samples for Moisture, Analysis, Sizing, etc Residues Masses, Samples for Analysis

Dusts and gases Reject Solutions Slags

Flows, Samples for Analysis Masses, Samples for Analysis

Custody transfers of material from one operation to another, (e.g. from a mine to a concentrator, from a concentrator to a smelter, etc.) follow the generally accepted practice of utilising the receiver’s mass, sample and analysis. Where these transfers occur within the same company, the Home and Away System (double sampling and analysis), whereby both the receiver and the dispatching operation independently weigh, sample, and analyse the material being transferred, ensures that gross ing errors do not occur. The dispatching operation will record both the dispatched mass and the metal contents and those determined by the receiver. The receiver’s quantities will apply and any discrepancy is recorded as an uned loss or gain. Where serious discrepancies between the quantities reported by the two operations occur, they must be investigated immediately and corrective action taken. Custody transfers of material between independent companies, e.g. toll smelting, refining or metal purchase, require the of the transfer be set out contractually. A typical approach is that the receiver and the sender will separately analyse portions of the receiver’s sample, although the Home and Away System, described above, is recommended. The results of the separate analyses are exchanged and compared by the two organisations, at a pre-determined time and date. Differences between the two results for each analysed metal are compared against splitting limits, which are set out in the governing contract and they are based on the estimated precision of the weighing, sampling and assay processes involved, as well as the level of risk involved in the metal transfer. Results which fall within the splitting limits are usually averaged, while those that fall outside these limits are usually re-analysed, settled by mutual agreement between the parties to the contract, or sent to a third, Umpire Laboratory, chosen by the parties. In the event of an umpire laboratory being used, final settlement of disputed analyses is reached by comparing the umpire analysis with those of the contract parties, based on procedures laid down in the contract.

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7.3

METAL BALANCING

In the past, Metal ing has been subject to many technical limitations in of mass measurement, sampling, assaying and, in particular, computational power. The last few decades have seen substantial improvement in all areas of measurement and computational power has ceased to be a constraint. However, the traditional methods described below are still in common use. A distinct terminology has developed to indicate which calculation method is likely to have been used. These and their associated calculation methods are defined below. Evaluation involves activities, some technological and some statistical, concerned with the technical quality of the accepted data and it normally involves the comparison of two independent measurements of the same quantity. Recovery and ability are separate but related parameters, the determination of which is necessary for the efficient monitoring of the performance of metallurgical processes and for reliable forecasting and planning. Various formulae can be used in metal ing exercises to calculate recovery, depending on the specific application, although certain formulae are preferred and specified for this Code. However, the most important issue is that the chosen formulae should be used consistently and not changed arbitrarily to suit specific needs at the time of reporting, as these formulae use different combinations of the same data to give different and confusing figures for what is believed to be the same value. It is also necessary to define the boundaries over which recovery or ability is measured, and over which all inputs and outputs are measured, and this must be included in the recovery or ability statement to avoid any confusion. Recovery, calculated on the basis of all process inputs, is defined as Actual, as shown below: Actual Recovery (%) = (sum of all useful or desired outputs) x 100 (sum of all inputs)

[Eqn 7.3]

The above expression forms the basis of the recovery calculation for the Check inCheck out approach required in of the Code. An alternative approach is to use Built-up Recovery (sometimes called Retrospective Recovery), and it is calculated based on all process outputs, as set out below: Built-up Recovery (%) = (sum of all useful or desired outputs) x 100 (sum of all outputs)

[Eqn 7.4]

Another alternative definition used in some metal processing industries is Theoretical Recovery, which is defined by the expression: Theoretical Recovery (%) = (metal input – metal in losses) x 100 metal input AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

[Eqn 7.5] 75

Any reference to recovery not qualified by a statement of the type and boundary of that recovery can be ambiguous and, therefore, meaningless. Use of any recovery calculation method other than the Actual Recovery defined above must be motivated via an Exception Report and approved by company management in of the Code. ability is defined by the following formula: 1 ability (%) = (sum of all outputs + stock and inventory change) x 100 (sum of all inputs) [Eqn 7.6] This, too, must be defined in of boundaries and it provides a check as to whether over time, inputs, outputs and stock changes reconcile. It must be accompanied by a clear indication of its confidence limits, as calculated from the constituent measurements. An alternative form of the above expression for ability which is also used is: 2 ability (%) = (sum of all outputs + Closing stock) x 100 (sum of all inputs + Opening stock) [Eqn 7.7] Expression 1 provides no credit for dormant process stocks whereas Expression 2 provides 100% ability for such dormant stocks. If these dormant stocks are large relative to the throughput in the ing period under consideration, Expression 2 enhances the ability figure, bringing it closer to 100% and it provides an incentive to hold and manipulate large plant stocks or plant inventory. As the purpose of a metal ing system is to measure the ability over a specified ing period, use of Expression 1 is required in of the Code. Any material received by a plant from a previous processing operation, but which has not yet entered a process stream, or any material produced by that plant to dispatch standard quality, but which has not yet been dispatched, is generally defined as plant stock. Any material which has entered the process stream, but which has not yet been produced as a product for treatment in the next process, is generally defined as process inventory. The day-to-day data collected from a metallurgical plant is rarely consistent and it will usually contain redundant information. In general, any two methods of calculation will yield different results and, therefore, many factors influence the selection of a suitable metal ing strategy. These include the degree to which it is practical (and economically justified) to weigh and sample accurately the feed to, and the products from, a processing operation to achieve Check in-Check out, and the availability of redundant data. An important additional factor is the degree to which the metallurgical ing system s process performance evaluation and optimisation. There are some general guidelines. Mining operations and deposit models are usually relatively sparse in of available redundant data. By way of contrast, and recognising the difficulty in accurate mass measurement and sampling of ROM ore, a modern mineral processing operation usually enjoys a wealth of redundant AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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data, especially downstream of the main size reduction process. Reducing the particle size and transferring the ore past a single point, greatly simplifies mass measurement and sampling. Similar comments are often true for hydrometallurgical plants. When the material reaches the pyrometallurgical plant, the situation may well reverse with data becoming scarce and being limited by risk of access to hot metal and by toxic off-gases. Table 7.2 below indicates some strategies commonly used for various types of operations in the metallurgical industry. However, each situation is unique and should be carefully assessed. While the dominant purpose of metal ing is to manage Custody Transfer, it is recommended that the same process be used to monitor process efficiency and intermediate process inventory. It is essential to ensure that the Custody Transfer measures are within the expected limits. For concentrators, if limited data is available, the Two-Product Formula (Section 7.5.1.1) or the Three-Product Formula (Section 7.5.1.2), as appropriate, can be used to provide some independent indication of the validity of the selected measurements. If the measurement accuracies are known and there is sufficient redundant data, the mass balancing reconciliation measures outlined in 7.4 can provide a robust means of checking on the measurements used for the balance. If two measurement methods agree to within the desired statistical criteria, the Custody Transfer measurements can be accepted as being "correct", within the limitations of the measuring system. If the measurement methods do not agree to within the specified criteria, the mass balance can often provide detailed guidance to identify the source of the problem. Table 7.2: Strategies for Metal Balancing Type of Operation

Examples

Type 1:

Smelter, Metal Refinery

High grade / high value feed.

Recommended strategy is Check inCheck out.

Medium Flow rates. High Value Product. Significant plant inventory. Weigh and sample all inputs: Check In. Weigh and sample all outputs: Check Out. Type 2:

Gold Operation

Low value feed – medium to high flow rates, often of coarse feed.

Recommended strategy is to reconstitute tailings at measured feed rate and production.

High value product. Low mass, high value plant inventory. Weigh and sample product. Type 3:

Base Metal Concentrator

Low value feed – medium to high flow rates – sampled after primary grind.

Recommended strategy is to mass balance feed analyses, concentrate

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Type of Operation

Examples

Low value product – sampled as concentrate shipments.

analyses and tailings analyses at measured feed rate and production rates.

Low plant inventory. Conveyor weigh-scale or weigh feeder for feed mass into plant. Accurate sampling of fine tailings. Type 4:

Coal Operation, Iron Ore

High tonnage of feed and products.

A commodity sales contract will detail prices and penalties as well as acceptable measurement techniques for both producer and buyer.

Minor rejects. Low plant inventory. Sometimes direct shipment to customer. Detailed product specifications. International standards for sampling and characterisation.

Recommended strategy is to weigh and sample feed and product streams.

Custody transfer mass will often be a draught survey or a train load out weight. Type 5:

Precious Metals Refineries

High value, low tonnage of feed.

Recommended strategy is Check inCheck out

High value products. Minor reject streams. Significant plant inventory. Weigh and sample all inputs: Check In Weigh and sample all outputs: Check Out Type 6:

Aggregates

Low value, high tonnage feed.

A commodity sales contract will detail prices, usually based on tonnage and size specification.

Minor rejects, usually based on particle size or density. Minimal plant inventory.

Recommended strategy is to weigh feed and product streams.

Type 7:

Heap Leach

Low grade, high tonnage feed.

Weigh, sample and analyse feed and product.

Residues remain in situ in some cases. Very high, difficult to measure, process inventory.

Perform accurate solution balance.

Non steady state, two phase slow reaction kinetics.

Periodic checks of heap solution inventories and heap residue metal contents.

Type 8:

Industrial Minerals

Medium to low value, high tonnage feed.

May be treated as a commodity.

Low process inventory.

May be produced as different grades.

Require use of Mineralogical analysis.

Weigh feed and product streams.

Products may be in bulk, bags, or other containers.

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RECONCILIATION OF METAL BALANCES

7.4

The challenge for a metallurgical ant is to evaluate and establish the precision of all the input variables used and of the resultant balance, to identify and reduce errors and to improve the accuracy and confidence in the balance whilst ensuring that data is both self-consistent and as accurate a representation of plant performance as possible. Techniques to achieve this, such as:

•

The use of Secondary ing

•

The calculation of stream values from different components and sources

•

The use of tie substances, which unchanged through the process

•

The use of CUSUMS to identify problems

are discussed below and in the sections which follow. All plant measurements contain some usable information and effective corporate governance is greatly enhanced by better knowledge of which measurements are the most valid. Knowing the precision of each measurement, the general mathematical solution to the fundamental problem: “Is what comes out equal to what goes in?” becomes a more detailed question: "How much should each measurement be adjusted (relative to its measured precision) to generate a mathematically exact balance?" A mathematically exact balance means a balance where all of the balance flowsheet constraints are satisfied and total and component masses are consistent across the complete balance with no uned losses or gains. To make statistical statements about the balance and the results that are found, it is necessary to adopt a specific approach to the solution of the mass balance problem. A simple approach to the problem in which both mass flow and assays are involved in a balance around a plant or around units within the plant is given in the paper by Lyman (Appendix E3). In this paper, the data adjustment problem is formulated as a mass flow adjustment problem, where the mass flow is the product of a measured flow rate and a measured assay. The assay adjustments can be back-calculated from the adjusted flows, but this detail is omitted for brevity. The changes which are applied to each measured value to achieve this balance are usually called Adjustments. When adjustments to an analysis or a mass flow are divided by the estimated standard deviation of the corresponding measurement, they can be described as 'weighted' adjustments. If all the measurements which are employed in the metallurgical balance are relatively accurate, the expected value of any one adjustment is very close to zero. The distribution of the adjustments will also tend to be normal, but all adjustments calculated from the balance will be statistically dependent or correlated. One might also think that the weighted adjustments should have unit variance because they have been divided by a standard deviation, but this AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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is not correct because the balance equations introduce constraints between the values of the adjustments. Any balance problem will have a certain number of degrees of freedom that depend on the interconnection of the process streams and on what has been measured (mass flows and number of species involved in the balance and whether or not total mass flows are balanced as well as the species flows). The total sum of squares for the adjustment problem is approximately distributed as a chi-squared random variable with the number of degrees of freedom that the particular adjustment problem possesses. As such, the expected value of the total sum of squares is approximately equal to the number of degrees of freedom. The true expected value of the sum of the weighted adjustments may vary from zero because, for example, the simultaneous adjustment of mass flows and assays in flow sheet is a non-linear problem. However, in many cases this expectation can be taken to be zero. To test whether or not the weighted adjustments being applied to the data are too big, advantage may be taken of the fact that the weighted sum of squares of adjustments is approximately chi-squared. Then, a goodness of fit test can be applied to the weighted sum of squares value. If it exceeds the chi-squared value at, say, 95% confidence for the appropriate number of degrees of freedom, there is an unresolved problem. Either the estimates of the data precision are incorrect or some combination of measurements has a precision lower than stated. One or more values used in the balance may be biased. The cause for the excessive weighted sum of squares should be investigated. Under such circumstances, an Exception Report must be generated. It can be difficult to pin down which of the measurements causes the increase in the chi-squared value. The best approach is to conduct the balance successively with more and more analyses included in the balance, or with different combinations of analyses. This strategy may show up the problem quite quickly. The important statistical results with a case study are provided in Appendix E.3. An alternative approach to balancing data is provided in Appendix E.1 The examples provided do not consider internal stockpiles (or bin inventories for example). However, they can be included in the balance by creating an input stream for draw-down from a stockpile and an output for build-up of a stockpile. It is critical that the accuracy with which these inputs and outputs can be estimated is determined. In many cases, the accuracy on the draw-down or build-up will be low and adjustments to these mass flows will represent the majority of the overall adjustments applied to the data. The overall confidence limits attached to the period balance will be correspondingly wide with a concomitant increase in the risk associated with the balance. A high risk level may necessitate an improvement in the way these stocks are handled. Each change to the raw data must be recorded and justified, and then authorized by the responsible senior management appointee. An audit trail must be available to trace the path from the initial balance with its associated recovery and ability, AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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to the equivalent values in the adjusted balance for the ing period and this information must be available for audit purposes. The adjusted balance must clearly indicate the magnitude of any adjustment.

7.4.1 Detecting Random Error (or Illegitimate/Spurious Errors) If a measurement has been adjusted by more than twice its estimated standard deviation, then it is worthy of further examination. If a subsequent balance, which omits this measurement (or increases the estimate of its standard deviation), shows a significant overall improvement, the measurement is likely to be in error. However, some investigation is usually required to ascertain whether the error is random or spurious – or even a larger than usual bias.

7.4.2 Bias The problem of bias is important and may not become apparent until many balances have been carried out. The objective is to identify and to eliminate bias, not to adjust data, and thus conceal the bias. For a given balance structure, it is possible to calculate the variance of the arithmetic sum of the weighted adjustments for each analysis type (this calculation is not detailed in E.3, but it can be derived from the results there; it will be applicable to that type of balance only). With this estimate of the variance for the analysis type, it is possible to test the significance of the sum of the adjustments. Appendix E.3 also provides the covariance matrix of the adjustments and the diagonal elements of that matrix provide the variance of the adjustments. These two can be used to test the magnitude of individual adjustments. Note that the adjustments should be tested against their variance, not the variance of the original measurement. The comments about detection of a bias (1.7.2) which is small relative to the accuracy of measurement for a single ing period are still valid. The relevant Cusums can be of adjustments (or of weighted adjustments) for each measurement. A graph, which shows measured and adjusted data plotted together against successive ing periods, will often show a bias very clearly. That is, instead of crossing on a reasonably frequent basis, one line will lie above or below the other for many ing periods.

7.4.3 Estimating Flow Rate Accuracy The general approach described in Appendix E.3 provides estimates of variance for each flow rate in the balance which are intrinsic to many reconciliation methods. However, for other methods some random variation can be added to each measurement or adjusted value and the solution re-calculated many times. This is called a Monte Carlo approach. The variation of flow rates, or parameters such as ability and recovery, provides an estimate of the variance of each. In qualitative , a small variation in a well-defined flow rate will produce a much larger variation in the sum of squared adjustments than a similar variation in a poorly defined flow rate. If a flow does not produce any variation in the sum of weighted squared adjustments, it is very poorly defined.

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7.5

METAL BALANCING APPLICATIONS

7.5.1 Concentrators with minimal accumulation Metal ing in concentrators is based on one of the recovery expressions referred to in Section 7.3. The specific expression used, actual recovery (required in of this Code) or built-up recovery, will depend on the information available to the plant management and, in particular, on where the primary feed sample is taken. Ideally, where sufficient information is available, both expressions are used and compared to identify ing discrepancies, but only one will be used for ing reports. As there is no significant accumulation of process inventory in this type of concentrator, ability is measured by comparing all outputs with all process inputs over the ing period. As values of recovery, concentration ratio and enrichment ratio (c/f) can be determined from analytical results alone, the two-product formula is often used to provide information for plant control, although this will be retrospective, depending on the time taken to receive and process assay results. Direct control can be achieved by the use of on-stream analysis systems through which values of c, f, and t can be continuously measured and computed to provide up-to-date values of metallurgical performance. There are many methods used to for a plant’s production and most concentrators produce a metallurgical balance showing the performance of each shift. These balances are accumulated over a longer period (eg: daily, weekly, monthly, annually) to show the performance over the period in question. The actual recovery over the ing period is declared and any discrepancy between that and the ability is regarded as an uned loss or gain. Physical losses do occur on any plant and every effort should be made to keep these as low as possible at all times. Care must be taken to ensure that the balance is not compromised by recycle streams and the return of plant spillage to the circuit especially if these are of higher or lower grade than the main stream.

7.5.1.1 The Two-Product Formula Typically, in concentrators, it is possible to obtain reliable measurements of the feed (once the ROM ore has been reduced in size) and concentrate masses, but it is usually more difficult to measure the mass of the tailings stream. In order to assess concentrator plant performance on a shift or on a daily basis, and to control the operation using the evaluated results, it is necessary to for the products in of material and contained component masses. Thus, if the masses of the feed, concentrate and tailings are F, C and T, respectively, and their corresponding assays are f, c, and t, and it is assumed that there are no closure errors in the mass balance (i.e. F – C – T = 0), the two-product formula below can be used. (If the tailings mass is also accurately known, any closure error can be reported as an uned loss or gain of material). Therefore: And: Or:

F=C+T Ff = Cc + Tt Ff = Cc + (F – C)t

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This gives:

F/C = (c - t)/(f - t)

[Eqn 7.8]

where F/C is the concentration ratio. Therefore, C/F = (f – t)/(c – t) and plant recovery = (Cc/Ff) x 100% = 100c(f – t)/f(c – t)

[Eqn 7.9]

Equation 7.9 is known as the Two-Product Formula

7.5.1.2 The Three-Product Formula In some concentrators, typically lead-zinc concentrators, for example, more than one concentrate is produced in addition to the tailings. Using the example of a lead-zinc concentrator, if the weights of the feed, lead concentrate and zinc concentrate and tailings are F, P, Z, and T respectively and their corresponding analyses f, p, z and t, then F=P+Z+T i.e. material input = material output If one of these weights is known, the other three can be calculated, provided that two component balances are available, i.e. Ff1 = Pp1 + Zz1 + Tt1 Ff2 = Pp2 + Zz2 + Tt2

[Eqn 7.10]

where f1, p1, z1 and t1 represent the analysis of component 1 and f2, p2, z2 and t2 represent the analysis of component 2. Hence, application of the threeproduct formula requires two component analyses on each product. From these calculations, the concentration ratio and metal recoveries can be calculated in a similar manner to the two-product formula above. The three-product formula has its limitations. The equations assume steadystate conditions and the fundamental assumption that input is equal to output. This is true over long periods but it may not be true over shift periods. The three-product formula is subject to appreciable errors when the components are poorly separated and it is preferable to obtain accurate mass flow measurements of products rather than to infer them in this way. The formulas of three or more products are more sensitive to small errors both in data and calculation, especially when one of the products is of relatively small weight and low concentration, and this can lead to serious balancing errors (Taggart 1945). Plants using this approach typically prepare three-product formulae metallurgical balances on a daily basis. These daily balances are accumulated over the ing period, in order to obtain the weighted average analyses of the streams for the period. It is obvious that the values of concentrate and tailings weights calculated by this method are totally dependent on the analytical values. Because of inaccuracies in obtaining representative samples from continuous flow streams, and due to errors in AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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analysis, the calculated values of concentrate and tailings weights can vary, depending on the components used to determine the balance.

7.5.2 Concentrators with Intermediate Accumulation Over a typical ing period (e.g. a month) where a change in inventory in a concentrator occurs, it can be quantified by assessing the amount of concentrate retained in the plant in thickeners, tanks, filters, etc. and in stockpiles in the concentrate handling and dispatch area. Although sampling and analysing the stockpiles and other areas of accumulation can assess the metal content of the inventory, it may be more accurate to rely on the weighted concentrate analysis produced from daily or shift plant balances. Where accumulation in plant inventory occurs, the determination of the concentrator recovery has to take this into . The two-product formula to be used is then: Recovery (%)

= [(Cc + change in inventory) x 100] / Ff

[Eqn 7.11]

7.5.3 Coal Treatment Plants In the coal industry the practice of metal/commodity ing, on a full Check inCheck out basis, is not rigorously practised, in the sense of tracking a valuable component in the plant feed/ore to a final product containing an enhanced concentration of that valuable component. A metal/commodity balance could be performed on the basis of an ash balance. However, this would require extensive sampling of all waste streams, which is not generally the practice in coal preparation plants. Reconciliations could be completed to cross check declared yields at fixed size ranges against a ROM washability, although this would be of limited use, as it does not simulate the actual separation process. Materials balances are generally completed on an air-dried basis, which tracks ROM coal as it leaves the pithead, to the point of delivery to the final customer as a final product. Balances to for the reject materials can be completed but this is done to varying levels of detail, depending on the value it is considered to add to the ing process. Whilst the formal application of metal/commodity material balancing has limited application in coal, there are nonetheless stringent requirements regarding the mass measurement and sampling of coal (Refer to Appendix C.2.2) as contained in various international standards. Emphasis is placed on achieving the desired consistency of the quality of the coal and the implementation of quality control systems such as those defined in the relevant ISO standards.

7.5.4 Pyrometallurgical Plants Pyrometallurgical Plants include roasting, calcining and smelting processes. Smelters introduce phase changes into the processing chain, in that feed materials are melted and chemical reactions, usually involving oxidation and reduction, take place to separate the valuable components from the gangue and impurities, thereby AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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upgrading them. The products normally proceed as feed material to the next stage of refining. The usual process stages are:

•

Feed material preparation (such as crushing and drying), roasting or calcining, often followed by smelting with various fluxes and/ or reductants in a furnace

•

Withdrawal of a molten matte, alloy or metal from the furnace and upgrading and/or purification of this to product for dispatch by various means such as converting

•

Removal of molten slag for treatment to recover valuable constituents and/or disposal and gas and dust recovery. In some cases, such as with ilmenite smelting, the slag is a product for sale.

Feed The feed material depends on the purpose of the pyrometallurgical plant. This may be a reducing or an oxidising roast, smelting of sulphidic metal containing materials to remove gangue materials and iron, melting to reduce oxides (ilmenite smelting), the oxidation of unwanted species in the metal phase (steel making), or melting to separate the metal phase from the oxide phase. The gas phases may be air/oxygen or reducing such as carbon monoxide depending on the purpose of the smelter. Feed is often mineral concentrate or ore and reducing agent (anthracite/coal) and/or fluxes from an adjacent plant or from another supplier and it may be from the same company or it may be for toll treatment. However, many other materials can also be treated in a furnace owing to the nature of the process. These include recycle streams from elsewhere in the process and scrap. All these materials may contain moisture and it is important that appropriate samples are taken to establish the dry weight. In-house concentrate may be pumped from thickeners or transported in suitable containers or tankers as a filter cake or as slurry. This must be weighed in these containers or, if pumped slurry, measured by electromagnetic flow meter and density gauge and sampled with suitable equipment. It can also be measured by conveyor belt weighing or bin weighing after drying, before being fed to the furnaces. Ore or other feed materials must be weighed on delivery by road/rail and/or by belt weigher or weigh bins when processed. Pneumatic conveying is sometimes used for transport of the dry material to the furnaces. In this case the material is usually weighed in batches in weigh bins at the feed end or in bins on top of the furnace. The weight and analysis of the feed material should be compared with that from the supplying concentrator or mine. That weight may have been calculated from the two-product formula and/or measured on static scales such as weigh bins or weigh tanks. This usually means measuring or estimating the stock held in thickeners or stockpiles, which, as highlighted previously, is difficult and inaccurate. However, the comparison can highlight problem areas and arrangements can be made to reduce or eliminate the stock periodically to provide an absolute stock value.

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Which of these values for the inputs should be used on an ongoing basis for the metal ing calculations should be decided based on an assessment of the accuracy of the equipment and the methodology used to measure the mass and take the samples at the various points. Toll treatment materials are processed under contracts, which specify the mass measurement and sampling methodology for custody transfer and which often include weighing and sampling in dedicated facilities , after crushing or slurrying, in order to obtain representative samples. Calculated contents are compared with those of the supplier and they are subject to agreed maximum tolerances (splitting limits and umpire analyses). Payment , toll refining charges and recoveries are based on the agreed calculated weight and content. Normally, the toll treatment company will guarantee a specific recovery within a certain time frame and it will deliver to the supplier the agreed quantity of product or market it as part of its own production. Thus any gain or loss resulting from the actual recovery achieved can reflect on the recovery of in-house materials. Products Products are usually furnace or converter matte, metals, metal alloys, and metallic anodes and they constitute one or more metal phases), an oxide phase and, in some cases, a gas phase. These are normally sold or sent to a refinery for further processing. This further treatment may be in-house or on a toll refining basis. In all cases the product, which may be crushed or granulated, or in the form of ingots or lumps, must be weighed by the supplier and the consumer on static scales, weigh bins or on conveyor weigh scales, and sampled/analysed by both parties. The calculated contents must then be compared and those for metal ing use must be agreed according to contractual or in-house procedures. This weight is usually the most accurate mass measurement in a smelter. However, the material often contains some moisture and this can introduce a significant source of error depending on the mode of storage, loading/unloading, transport and sampling and can change during these operations. Slag Furnace Slag is usually a discard from smelting operations, although often it is further processed to recover contained or entrained metals before being discarded. Normally, losses of valuable constituents are a relatively small percentage of the feed. Sampling and weighing of furnace slag depends on the manner in which the slag is handled after it is tapped from the furnace. Samples can be taken during the tapping operation from the molten material for plant control, but these are not recommended for metal ing use. If the slag is granulated or crushed before further treatment and disposal, sampling and weighing must take place during the conveying or trucking to treatment/disposal. In some cases, such as in the smelting of ilmenite, slag can be a product for sale and it should be weighed and sampled as for other smelter products. Converter Slag usually contains significant levels of valuable metals. In some operations it is discarded with the furnace slag, in which case it must be sampled and weighed in a similar manner to that used for the furnace slag, to ensure satisfactory metal ing. Normally, converter slag is subjected to further processing in slag cleaning furnaces before discarding or it is recycled to the smelting furnaces for AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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recovery of the contained metals. Transfer usually takes place as hot liquid metal in a ladle transferred by a crane. This transfer can be weighed by load cells fitted to the gantry cranes and sampled to meet internal plant or Secondary ing requirements. Dusts / Gases Dusts are recovered from the dust removal equipment treating the off-gases from the furnaces, converters or other process steps and the dust extraction equipment on the transfer points. This dust is normally returned to the process but it is not usually weighed and it is treated as an internal recycle. Some dust is lost to the atmosphere. This normally contains only a small proportion of the valuable constituents in the smelter feed and measurement of the mass and sampling on a periodic basis may be satisfactory, in these cases, to provide an estimate of the losses. Off-gases may be also recovered to utilise their waste heat or to recover pollutants or economic constituents. Sulphur Fixation In the case of sulphide smelting, a proportion of the sulphur is recovered as sulphuric acid, or as sulphur fixed in some suitable compound or, in some cases, it es directly to the atmosphere as sulphur oxides without any treatment. Sulphur is usually analysed in the off-gases and the final sulphur product is weighed and sold if it is sulphuric acid or some other tradeable product. Smelters operate under permits that limit the mass emission of sulphur to the atmosphere and they are subject to environmental regulations that specify permissible ground level concentrations of various pollutants such as sulphur dioxide. Thus a balance must be performed to calculate recovery and losses of sulphur. Metal ing Recovery and ability for smelters and pyrometallurgical plants are calculated utilising the same formulae as shown in Section 7.3. For Check in-Check out all input and output streams around the plant must be measured and sampled leading to the calculation of material balance closure errors for various species. These balance closure errors reflect the material that is uned. However, dust and gas losses are normally subject to a high level of uncertainty and slag quantities are often not accurately measured, thus Check in-Check out cannot be carried out for species affected by such losses and lack of measurement. Slags are not homogeneous, they may have metal or alloy inclusions, and may contain varying amounts of moisture, especially in the case of water-granulated slags. However, the metal content in gases, dusts and slags is usually only a small proportion of the feed and, as such, the effect of omissions on the balance should not be significant. The amount of effort put into improving the accuracy of measurement of dust and slag losses is dependent on their magnitude and value, risk potential and the requirements for environmental reporting. However, it is recommended that furnace slag be weighed before disposal or recycling rather than using truck factors, which are notoriously inaccurate, as this will provide more accurate information for closure of the balance.

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Smelters represent a challenge in of metal ing because of the difficulty in measuring in-process inventories, intermediates and recycles, which are often significant; these include material from ladles and refractory repairs and metal/alloy build up in furnaces. Furthermore, the materials in streams around furnaces and converters are coarse, highly heterogeneous, variable in grade and they may be molten. They are therefore among the most difficult materials to sample. Nevertheless, the ing system must either treat these materials as in-process stock or recycle them out and back into the process on a Check in-Check out basis. Suitable tie components such as iron or silica, which report solely to one stream, or whose distribution is known, which are abundant and which can be readily analysed to a high precision, can also be used to calculate multiple balances, to provide redundant data and to facilitate the solving of anomalies, especially where Check inCheck out is not performed. In some cases indirect measurements such as temperature can assist in providing additional data. The mass balancing of reducing agents such as coke or anthracite is important in many pyrometallurgical operations as they are a major cost component and critical to process performance. Thus a carbon balance is important. Stocks are particularly difficult to measure accurately and they should therefore be kept to a minimum and, ideally, they should periodically be run down to zero since month-to-month recoveries can give a distorted picture of the metal balance. One practical way of calculating recovery over a period is to obtain a balance closure for a minimum of three major elements; this will provide redundant data and thus enable problem areas to be identified. Internal, Secondary ing between process stages is complicated by the recycling of materials which are normally returned to the furnaces (sometimes in molten form) and which are often not weighed and sampled. Dust from the dust recovery equipment is also recycled to the furnaces and it is also often not weighed. Thus the individual balances over the various process steps, required for plant performance, are more complex than for the smelter as a whole. However, the balance around individual process steps can assist in strengthening the reliability of the Primary balance. There is also the possibility of combining one or more process steps where that combination will clarify the ing because the inputs and outputs can be well quantified. Care must be taken to ensure that the system design for the mass measurement and sampling, allows for recycle streams and for the intermittent return of spillage. In addition to the above issues, the inherent variability of individual furnace processes in relation to the accuracy achievable through appropriate sampling and analysis can either produce a bias in the overall balance or it can reduce the overall accuracy of the balance.

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7.5.5 Hydrometallurgical Plants Hydrometallurgical plants are often the last stages in the processing chain to produce saleable products. As such, they are usually relatively complicated with multiple stages, many circuits for purification and upgrading and several recycle streams. In addition, Metal ing is often required for more components than in the earlier stages of processing, including for multiple products, residues, impurities, reagents and effluents. Processing will normally involve dissolution or other preparation of the feed material, separation and purification of the valuable constituents and the production of the final products as metals, salts or solutions and thus measurement is required in solid, liquid and, sometimes, gaseous phases. The liquid phases may include metals in organic materials as well as aqueous solutions. Feed Hydrometallurgical plants typically treat a solid feed material, such as ore, flotation concentrate, matte or other intermediate materials, including metal alloys, scrap metal, metallic concentrates and a variety of recycled materials in solid or liquid form, all of which may contain some moisture. The materials may be products from an earlier stage of processing from the same company or they may be purchased for toll treatment from outside companies. The comments in Section 7.5.4 on Smelters are equally applicable for measuring, handling and treating these materials. Products The feed materials are processed to give product metals, salts, oxides or other metal containing materials, as well as process effluent streams, and, often, intermediate products for treatment at some other facility. The solid products can normally be weighed and sampled to a high degree of precision by using static weighing methods during the final packaging into drums, bags or bales and again during dispatch, giving check weights. of sale will usually specify the acceptable precision and that mass measurement instruments must be certified for custody transfer. Solutions must likewise be weighed in the drums or tankers on certified equipment. In some cases, products may be solid residues sold for further processing. These may also contain moisture or solution and they may be in the form of slurry or filter cake. Measurement of process effluent streams is critical to establish losses of valuable components (including reagents) and impurity content in order to conform to environmental permits and good corporate governance as discussed further in Section 7.7. Metal ing As process streams can be made up of either, or both, solids and liquids with both containing significant quantities of metals, the metal ing system for such plants must make adequate provision for the sampling and mass measurement of all components of each feed and output stream and it must allow for the measurement and assessment of continuous recycle streams and of process spillage which may occur on a periodic basis.

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Check in-Check out is essential for the feed and product streams including liquid and gaseous effluents. The precise measurement of the latter is difficult, but usually the mass content of these streams is relatively small. Mass balancing of the individual process steps and circuits is important to monitor process performance and can provide checks on the Primary ing. Depending on the specific operation, the feed material is usually stockpiled ahead of the process to ensure continuous operation. Estimation of the metal content of the stockpile, however, can be a significant factor in overall process metal ing. Similarly, hydrometallurgical plants usually have a significant process inventory, in the form of metals in solutions and in solids, including the inventory in solvent extraction and electrowinning circuits. Estimation of this inventory is complicated by the changing metal contents of the solid and liquid phases as they progress through the plant and it has to incorporate tank level measurements, slurry pulp densities and solution and solids analyses at each stage of the process. The presence of a large stockpile of feed (or any other semi-processed intermediate) material complicates this estimation. In plants treating high value metals, the process inventory estimate should be verified at prescribed intervals by physical stock-takes, using the procedures such as the ‘bubble’ method, to reduce the probability of major stock write-offs caused by undetected errors in the routine ing system. Determining metal recoveries in these plants has to be based on the company’s requirements and guidelines but it must be calculated on a consistent basis. For example, a plant recovering several metals from one or more feed materials may decide to base all recovery figures on the primary product metal only, as this could be the basis on which all financial reporting is carried out. The procedures should also clearly define how the recovery is calculated, to allow for changes in process inventory and to define how metals contained in intermediate products for treatment by third parties should be handled. For example, the procedures should clearly state whether the metal in these intermediate materials should be treated as product metal, or whether the recovery calculation should allow for future losses of metal from these streams as they undergo further processing. Hydrometallurgical plants are subject to losses of valuable materials in effluents and they also normally generate various solid residues, which will contain some metal values in both solid and solution. Effluents are treated to minimise the losses, to recover as much of the valuable constituents as possible and to remove deleterious impurities before disposal. Similarly, solid residues may be retreated before being discarded, sold or recycled to earlier stages of processing. Besides the valuable constituents, it is necessary to measure, for and report on various impurities and pollutants to ensure conformity with environmental regulations.

7.5.6 Heap Leaching The heterogeneous (two-phase), non-steady state nature of Dump and Heap Leaching, together with a protracted leach cycle (several months) and the consequent large process inventory all complicate metal balancing. In addition, the fact that the process inventory includes unleached, partially leached and fully leached ore, as well as solution with varying metal contents, and that a significant proportion AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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of the metal is not recoverable, raises issues that are not encountered in other processes. (In-situ leaching has the same problems to a much greater degree, but it is not covered in this Code, as it is regarded as a part of the mining process). Although the principles which follow can be applied in theory to both dump and heap leaching, in practice they would be impossible or not cost effective to achieve for many dump leaches where the metal content of the dump is not known with any degree of accuracy, and which cannot be adequately sampled, where the material is of low grade or value and high mass and thus Check in-Check out cannot be achieved or is not economically justified. Therefore, the recommendations in the following sections will refer to heap leaching only. In large heap leaching operations, there are often many piles being operated in parallel but at different stages of the leaching cycle. The large inventory also means that the effect of input or other changes is subject to long delays and it is not readily apparent. Process inventory in heap leach piles or pads has the following components:

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Metal in solid form in the material (undissolved but not recoverable)

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Metal in solid form in the material (undissolved but recoverable)

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Metal in solution which has been leached as the solution percolates through the heaps

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Metal which has been dissolved into solution but re-precipitates in the leach bed, due to local conditions such as pH changes. This will slowly re-dissolve as conditions are corrected

Process inventory is normally reported as the second and third items above, as the total amount of the metal in the heaps is not relevant if it is not recoverable. The main concerns are the inventory build-up of metal in the leach solution and the determination and prediction of the recoverable metal in the feed material. The type of heap construction, as discussed further below, affects the determination and significance of these inventories.

7.5.6.1 Types of Heap Leach Single lift heaps The material for leaching is mechanically placed on gently sloping terrain in modules (which may be separated by berms) from which the drained solution can be measured and sampled separately from the others. This type of heap will tend to have fewer problems with inventory build-up. The individual modules can be monitored separately and the total area under irrigation remains fairly constant, and this leads to a more stable solution volume inventory in the heaps, with the content then changing as feed grade changes. These heaps can be washed (eluted), after completion of leaching, to remove most of the metals in the solution. It is usual to sample them by auger or drill to determine the completeness of leaching of the recoverable metal and the value of the residue.

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On/off “dynamic heaps” can also be used where the residue, after completion of leaching is removed, in which case sampling is easier and more accurate. Multiple Lift Heaps These are similar to the above, except that material is placed on top of the first heaps after a period of leaching has taken place. This process may be repeated several times. Thus solution inventory will always increase as the leach solution saturates the new material and permeates into the earlier deposits. In addition, the fact that the leach heaps are usually higher may affect permeability, thus further increasing inventory and affecting recovery. Valley-fill Multiple Lift Systems These heaps are placed in valleys and the determination of inventory is problematic, as the monitoring and control of the solution is difficult due to the shape of the terrain and the fact that pregnant liquor may be retained in the heap.

7.5.6.2 Mass Measurement Sampling and Analysis of Process Streams Feed Feed material can be Run of Mine Ore from opencast operations and/or dumps of low-grade ore that have been stockpiled. (In the latter case the treatment is sometimes of a secondary nature for material that was not initially economic to treat). This material is usually crushed before being placed on the heaps but it may be agglomerated if it is too fine to permit an adequate flow of solution through the heap. Determination of the mass of material, and sampling to determine the metal content of the material being placed on the heaps, are both very important. They must be carried out according to prescribed procedures which must comply with the requirements of this Code and Guidelines. The most suitable place to perform these measurements is on the crusher product conveyor, before any water and additional reagents (e.g. acid, lime cement) are added, where adequate weighing and sampling equipment, as defined in this Code, can be installed. In the case of run of mine ore, blast hole sampling measurements should also be available and these figures should be reconciled with the crushed ore samples and the block ore grades. In both cases, the material must be tracked to the leach heap to provide the amount of material and metal or recoverable metal placed on the individual cells or modules of the heaps and pads. This is also important in order to generate information which can be used to predict performance for later operation. Samples of feed, crushed to the size deposited on the heaps, should also be taken for leach tests on weekly or monthly composites, to predict the recoverable metal and reaction kinetics for the material being deposited and for comparison with the original feasibility test work.

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Solution Balance Monitoring and Management The pregnant solution containing the dissolved metals is often stored in dams before being withdrawn for further processing. The barren solution after processing is usually returned to make up the leach solution used to irrigate the heaps. The solution metal balance for each heap or module is the first indication of the heap performance. Irrigation solution and drained pregnant solution from each heap must be accurately measured by suitable flow meters, which can be calibrated according to recognised techniques and sampled for analysis, according to this Code. Reconciliation of these flows is important in order to provide a calculation of the solution inventory in the leach piles, the metal extraction from all the heaps or modules under irrigation, and total plant production. Performing this reconciliation requires estimates to be made of the amount of rainfall, evaporation and the solution content of each abandoned heap. Rainfall and evaporation should be based on actual site measurements over the ing period. The solution content of operating, abandoned and resting heaps should be periodically established by test work, although standard figures based on experience are often used. It should be noted that the metal content in the solution in the heaps is normally the largest component of the total solution inventory and it can be equivalent to several months production, thereby emphasising the importance of this figure to the metal balance. Metal ing An accurate solution metal balance must be obtained, so that the total metal recovered from the material abandoned can be calculated. The importance of an accurate measurement of the metal placed on the heap was highlighted above. However, the ing is complicated further by the fact that not all the metal is recoverable, and this non-recoverable metal can form a significant percentage of the total, leading to a very large process inventory. It is common practice, therefore, to use the recoverable metal placed on the heap, rather than total metal, for process control and for mass balance and performance calculations. If recoverable metal is also being used for ing, then this must be approved by means of an Exception Report. This recoverable metal should be determined, for the specific material being placed, by suitable laboratory tests such as rolling bottle or shake tests, or pilot scale column tests, on representative samples. These values are then adjusted by factors, based on experience in older heaps, to reflect the predicted ideal performance of an operating heap. The procedures and methodology for performing these tests and calculations should be rigorously designed, documented and monitored. Thus, the balance will reconcile the output metal in solution with the inputted recoverable metal to give the recoverable metal still in the heap in both solid and solution forms. To achieve a Check in-Check out balance, when a particular pile/heap has reached the end of its life after leaching and washing has been completed, a closing balance must be performed and the heap sampled to check the amount of total and recoverable metal remaining. This sampling should be AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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performed by mechanical drill/auger or preferably by sonic drilling on a regular grid designed in accordance with sampling theory to give the requisite number of samples for a meaningful result if the material is to be left in place. If it is to be moved, such as in the case of an on-off heap, it must be sampled by approved methods as it is being removed. If it has been decided not to perform Check in-Check out, such as when the metal leached has been calculated solely from the solution content subtracted from that fed to the heap or pad, then this must be approved in an Exception Report. The metal that was not recovered must be removed from the plant inventory when a heap is abandoned. If variations in feed grade are small, then, as many heaps are operated at one time, the process inventory will tend to stabilise as heaps are established and abandoned. Sampling of heaps is subject to a high level of inaccuracy and due cognisance of this must be taken in the use of the information generated for stock evaluation and in predicting future performance. The metal balancing from the output leach solution through the purifying and refining steps to metal product is similar to that for hydrometallurgical processes shown in Section 7.5.5. General practice is to calculate the recovery as the metal finally produced, after the purifying/ refining process, as a percentage of the metal placed on the heaps. Process modelling based on laboratory, pilot plant and actual performance is important in order to improve the predicted recovery from a specific operation and, for metal balancing purposes, to enable better predictions of the recoverable metals and process inventory. Modelling can be empirical or deterministic and various modelling tools can be used. Empirical modelling is based initially on test work and it should be updated regularly, based on the actual performance of an operation.

7.5.7 Bench Scale, Pilot and Demonstration Plants 7.5.7.1 Introduction The throughput scale of pilot plant testing covers a large range from Bench Scale Plants through Pilot Plants to Demonstration Plants. Pilot plant work is undertaken for a number of different objectives such as:

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Determining process economics

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Performing process development

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Demonstrating technical feasibility

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Providing technical services

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ing market development

Pilot plants must be designed to meet the relevant objective. This type of testing is often desirable and it is required, both by in-house procedures and by external financiers, as part of the Risk Management process for a new metallurgical project or process, or for new stages of a process. Thus testing AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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is critical to the decision whether to proceed or not. Some or all of the scales of testing may be appropriate. Normally, testing would proceed from bench to pilot plant and then possibly to demonstration plant, with each stage of proving the process reducing the technical risk and demonstrating the feasibility on a larger scale. The degree and scale of testing required is dependent upon:

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the novelty and degree of understanding of the process or process stage

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knowledge of the metal content and impurities and behaviour of the material to be treated in the specific process

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the scale of the commercial operation

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the degree of risk, both financial and commercial, as well the risk to reputation.

The above considerations determine the scale-up factor to the commercial plant that is acceptable. This may vary from a factor of 10 for a new technology to 1000 for a typical well-known South African gold ore, for example.

7.5.7.2 Feed Materials Feed materials for pilot plants may be bulk ore samples that have been collected from a site by a drilling and sampling programme, or they may be partially processed materials from various sources that have been collected and brought to the test facility; however, they may also be a portion of a stream taken on an on-going basis from an operating plant, in which case, the pilot plant is located at that plant. In all cases, sampling procedures must ensure that:

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the pilot plant feed material is representative of the ore body or the material to be tested

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the sampling protocol must ensure that there is continuous traceability of the bulk sample from source to pilot plant

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the mass is measured and monitored according to the recognised procedures as required by this Code.

7.5.7.3 Design Metal ing for pilot plants must embody all the principles covered in other sections of this Code. However, in addition, the following factors must also be considered when deg the plant and the system for deriving the metal balance:

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The design of the pilot plant must take into all safety, health and environmental factors as well as company and regulatory requirements. Hazops analysis should be carried out

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The normal design process must be followed, such as process design criteria, PFDs, P & IDs, and equipment specifications, with adequate provision for mass measurement and sampling to the requirements of this Code

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The pilot plant can be used to establish extraction or recovery of the primary products as well the deportment of impurities, effluent quality and the consumption of reagents

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The plant can also be used to assist in proving materials of construction, and establishing process control philosophy and key measurement parameters

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Pilot plants are often operated under variable flow rates and conditions, in order to establish the optimum conditions for the full scale plant

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Bench scale tests may be batch, but a pilot or demonstration plant should operate continuously and appropriate streams be recycled

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Pilot plant circuits and designs are often based on scale up from laboratory or bench scale tests and circuits may be subject to change, thus flexibility must be allowed for

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The information obtained will be scaled up by some orders of magnitude to design the full-scale plant, thus errors will have an effect on both capital and operating costs, as well as on performance

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In many cases, the performance of the full-scale plant will be less efficient than the pilot plant because of inefficiencies in the larger scale equipment, and factors need to be applied for assessing the viability of the full-scale process. These factors can only be based on previous experience and company policy

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The generation of sufficient product for evaluation by potential customers may be required

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Pilot plants can be used to facilitate the training of supervisors and operators.

7.5.7.4 Operation The design and operation of the plant must allow for suitable equipment and procedures, in line with the Code, for mass measurement, sampling and analysis that will ensure that meaningful results to a known accuracy can be obtained for the metal balances and to facilitate the decision process. Operating and sampling manuals are required. As a fundamental principle, all input and output streams must be ed for, i.e. Check in-Check out and:

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ability must be better than 100 % ± 5 % at 95% confidence levels for the product metals and 100 % ± 10 % for impurities, for the results to be utilised for economic and process decisions

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as quantities are much smaller than a full-scale plant, static weighing on certified weighers only must be used to establish feed and product

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weights. Conveyor belt weighers or other approved mass flow measurement devices may be used for control of flow to the process but these must be reconciled back to the batch weights of feed determined by static weighing

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samples are a larger proportion of the process flows than in a full-scale plant and the mass and metal content of the samples must be included in the metal balance as part of the Check in-Check out

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similarly spillage must be collected, weighed, analysed, and ed for in the balance at the appropriate stage. It should not be returned to the circuit, as it causes process disturbances. Spillage should be kept to a minimum

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effluents must be collected, accurately weighed and sampled

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appropriate metal balancing methods must be used to estimate plant performance

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the entire sampling frequency and plan must be statistically designed to yield meaningful results, allowing for the fact that planned variations in operating conditions will occur and that different circuits may be tested, that a wider range of analyses, to a much better precision, is required than on a full-scale plant and that stage by stage performance measurement is often necessary (i.e. both Primary and Secondary ing)

•

pilot plant operation must be carefully supervised on a continuous basis. Each shift should be under the control of a graduate and it is essential that the operators are trained and have sufficient experience in order to ensure that the sampling, weighing and data collection are performed to the necessary standard

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the piloting campaign must be of sufficient duration (several weeks) to generate adequate data and care must be taken that steady state conditions exist for the period of the metal balance

7.5.7.5 Reporting Results The report on the pilot plant results and metal balance, especially in the case of proving process economics or project feasibility, must include the following:

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Information on the source and provenance of the feed material

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Information on the scale and throughput rate of the plant

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Information on the methodology used to obtain all data and analyses and the precision of the results

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A complete metal balance with a full Check in-Check out system

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An ability for the constituents in the feed of greater than the agreed minimum percentage as defined in Section 7.5.4

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An assessment of the accuracy of the overall balance

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•

7.6

A comparison of the repeatability of the various campaigns. A recommendation as to the factor to be applied to the key parameters of the results to allow for the inefficiencies of the large scale commercial plant.

MINE TO PRODUCT BALANCING AND RECONCILIATION

A common terminology used, especially in the case of precious metals, for assessing operating performance, is the ‘call’ system, in of which the amount of metal that is produced as final metal, and in the residues or tailings, is compared to the quantity delivered to the plant in the plant feed. This gives the plant call factor, which is usually expressed as a percentage, and it is a measure of the ability. In the same way, the measured mass of metal in the ore delivered to the shaft from the mining operations is compared with the estimated metal content in the ore mined, as determined by sampling and surveying, to give the shaft (or mine section) call factor, also expressed as a percentage. The combined plant and shaft call factors give the mine call factor for the operation. Typically, these call factors are calculated monthly and they form a useful means of monitoring discrepancies between the expected and actual mine output. However, the reconciliation of Metal ing back to stopes or ore blocks is outside the scope of this Code at present.

7.7

MASS BALANCING FOR ENVIRONMENT AND HEALTH MONITORING

The increasing level of public awareness, responsible corporate governance and emphasis on the triple bottom line has meant an additional focus on this area. Measurements are required to quantify stack or other emissions to the atmosphere and the resultant exposure of affected persons, as well as liquid and solid effluents (including storm water run-off) disposed of in treatment ponds and dams or discharged into rivers or the sea. The ability to monitor and report on the efficiency of energy utilisation is also required and the Kyoto Protocol has introduced the possibility of Carbon Credits for reduced greenhouse gas emissions. Some companies have adopted an objective of achieving a zero effluent status. The effective use of scarce water resources and, in some cases adhering to the use of permitted quantities, is another factor which makes an overall water balance desirable. All of these aspects may have a monetary value, in of penalties or credits and there may be risks to reputation, as well as legal risks to the company. For metal balance purposes measurement of streams from operating plants only is relevant. However, measurement and quantification of any dissolution of heavy metals and other contaminants from surface dump material, such as, acid mine (rock) drainage and percolation of critical compounds into aquifers or watercourses, or gases emitted and dust blown from slag and tailings dumps, is also necessary for corporate governance. An overall understanding of the balance of these components is desirable to provide information in order to take corrective action and make improvements as required in ISO 14001. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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7.7.1 Gases and Dusts Measurement is often required to provide a mass balance for impurities such as sulphur, greenhouse gases, nitrous oxides, trace elements and impurities such as arsenic, selenium and mercury, in order to establish the quantity emitted to the atmosphere and conformity in of the permits and regulations. Measurements are also required to determine ground level concentrations and the exposure of employees in the plants, or persons in adjacent areas, to gases or dusts containing toxic or harmful constituents. Mass measurement and sampling are normally relatively complex, requiring specialist equipment and often on-line sampling and analysis. The volumetric flow of gases and the solids content in the stack or ducting has to be measured and suitable samples withdrawn (isokinetically), separating solids from the gases for analysis of both, taking due cognisance of the flow profile in the conduit and the temperature. Similarly, ground level and personal samplers are required to determine atmospheric contaminants and employee exposure, separating and collecting solids and gases to determine mass flow and composition. Methods of measurement, sampling, and analysis are laid down and specified by organisations such as the EPA, NIOSH, WHO and other national bodies, as well as in national Air Quality Acts and other legislation. Companies should adhere to the appropriate regulations and guidelines. In many cases, companies have adopted international standards for worldwide application, even where local standards do not exist or are more lenient. Further assessment of these measurements is outside the scope of this Code at present.

7.7.2 Liquid Effluents These are normally pumped as slurries in the case of tailings or other residues, or as solutions, or flow in open drains or channels. Measurement may also be required to determine the run-off of pollutants (for example dissolved salts such as sulphates, fluorides or chlorides and metals such as nickel, hexavalent chromium, mercury or cium) from storage facilities or stockpiles through ongoing monitoring, and the possible contamination of the underground aquifer by the sampling and analysis of boreholes. Measurements tend to be more difficult than main process streams and the level of pollutants and methods of measurement are defined by international agencies as mentioned above and in relevant national Water Quality Acts and Regulations. Companies should adhere to these guidelines and/or approved worldwide standards. Further discussion of the methodology is outside the present scope of the Guidelines.

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8

REPORTING

The Metal ing system must generate suitable reports over various time frames and ing periods. The primary purpose of these reports is to:

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provide a metal balance of the desired constituents over the reporting period (this would normally be monthly, with quarterly or six monthly reports for the audited company financial reports)

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provide this information promptly to Plant Management and to the Financial ants

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highlight inconsistencies and problem areas in the reported data

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report the level of error in all the reported outputs and their comparison with target levels

As a secondary objective, the reports should be capable of being used to:

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assist in providing information for diagnosing problems in plant operation, performance and control on an ongoing basis, for shorter time periods than those required for financial ing (weekly or daily)

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assist in predicting plant recovery and outputs for future planning and project evaluation

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provide information on inaccuracies and inconsistencies in the input data

In order to meet these objectives the reports must:

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be prepared by personnel who are adequately trained and who understand the source and meaning of the inputs

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make use of data which has been subject to the minimum number of manual entries /re-entries. Ideally each piece of data should only be entered once or collected directly from SCADA, LIMS or similar systems

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have used reasonable, determined allowances for error for each of the inputs that have been generated, by recognised error determination procedures, where the accuracy and precision of the data have been determined by a recognised method

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have used an approved procedure to reconcile the mass balance, to identify inputs subject to illegitimate (spurious) errors, to determine bias, and to facilitate remedial action if warranted

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provide clear traceability back to the sources of all inputs

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be “cast in stone” and incapable of being adjusted or altered without the written authorisation of the responsible person. Any authorised alteration must be logged so that it can be seen by all

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be available to all who should have access, with suitable safeguards to prevent unauthorised access

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be as simple as possible and such that it can be adapted to suit changing circumstances and process flow sheets

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be reviewed and signed off by a metal ing committee, or other suitable authority, for subsequent approval by the Audit Committee of the Board

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comply with International Auditing Standards as set out in IAS 2

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clearly indicate the performance of key indicators such as ability and recovery with targeted levels

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give historical comparisons over a suitable longer-term time-frame to enable long-term trends to be identified.

An important point of custody transfer is the interface between the Mine operations and the Plant operations. The estimate of the metal delivered by the Mine must be reconciled with that which the Plant estimates it received. Normally, the receiving values are accepted but, periodically, adjustments to the plant estimates will be required when large discrepancies prevail. Therefore, the primary input to the plant may have to be adjusted, irrespective of its own measurements. The integrity of home-grown spreadsheets used for metal ing, which can be subject to modification by the metal ing personnel to suit specific reporting needs, can be a serious concern. The use of such spreadsheets for metal ing purposes must be avoided. Various commercial metal ing software packages are available and these should be used in preference. Where home-grown spreadsheets are used, clear rules for setting up such spreadsheets must be set down, stating that the spreadsheet must be contained in a single file, with no multiple linked files allowed, and with no incorporation of macros or any other sub-form of programming. This is to ensure that the system used is uncomplicated and that it can be serviced and maintained in the absence of the original designer. The rules must also state that the system must receive original input, which is to be independently verified, and that it must not receive input via links to any other file or system, other than data logging systems; this is to ensure that the ing system remains uncorrupted by spurious formulae, which may be introduced from other sources. The rules must further stipulate that no ing data may be altered unless the alteration is fully explained and motivated in a written report, which must also be approved at an appropriate level of authority, defined by the operation concerned, and retained for record purposes.

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9

SELECTION AND USE OF A COMPETENT PERSON

The Competent Person must have sufficient relevant experience and expertise to provide sound advice for the definition of realistic objectives in the setting up a metal ing system and for the identification of problems in an established system. The Competent Person must be formally appointed by the management of the company or operation concerned to undertake the auditing and setting up of metal ing reporting systems and must accept full responsibility for the system and must not treat the procedure merely as a ‘rubber-stamp’ exercise. The letter of appointment must clearly define the duties and responsibilities of the Competent Person including:

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Responsibility for final approval of the design of the Metal ing system

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Responsibility for auditing the system once it has been installed, to assess compliance with the design specifications

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Responsibility for periodic reviews and audits of the system, at predetermined intervals, to ensure continued compliance and, as required, to investigate problems which may occur

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Submission of reports to management in regard to the compliance of the system and monitoring of the action plans produced periodically to remedy faults

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Independence from the operation concerned.

The Competent Person should not be required for routine metal ing, or for the transfer of materials between organisations, but the Competent Person’s services should be used to ensure that a complete metal ing system, or a particular aspect of it, is working as well as can be expected. Often, in metal ing, one is in the hands of a few select people and problems occur, which could be material. It is recommended that an appropriately experienced person is involved to assist in solving these problems when they do occur. If the solution results in a change in the metal ing system, it is essential the change be reviewed and approved by the Competent Person. A Competent Person, for the purposes of this Code, must be a member of the relevant local, professional registration authority or any other statutory local or international body that is recognised by the relevant Code s. If a complaint is made in respect of the professional work of a Competent Person, the complaint will be referred to the recognised professional body with which the Competent Person is ed. A Competent Person should have a minimum of five years experience relevant to the type of metal or mineral under consideration and to the type of operation involved. The key word ‘relevant’ means that it is not always necessary for a person to have five years experience in each and every type of metal, mineral or metallurgical operation, in order to act as a Competent Person. For example, a person with twenty years experience in metal ing in a variety of base metals extraction and recovery operations may not require five years specific experience in a precious metals operation in order to act as a Competent Person. Experience in the other AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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operations would count towards the required experience in relation to precious metals. In addition to experience in the type of metal or mineral and the operations involved, a Competent Person assessing metal ing must have sufficient knowledge of sampling and analysis techniques, relevant to the metal or mineral under consideration, in order to be aware of problems that could affect the reliability of the data. Some appreciation of the mining operation and mineralization applicable to that deposit type would also be important. As a general guide, people being called upon to sign as Competent Persons must be clearly satisfied in their own minds that they can face their peers and demonstrate competence in the commodity, type of operation and situation under consideration.

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10

DESIGN OF METAL ING SYSTEMS

Metal ing data is generated primarily for management information purposes and not for financial reporting requirements. However, the outputs of the metal ing system do feed into the company’s financial reports and, as such, the system must be transparent and associated with a clearly defined audit trail. The design of the ing system should form part of the design scope for new plants and it should include careful identification of the mass measurement and sampling points required to enable a reliable metal balance to be determined. It should also include careful attention to the detail of the installation, calibration, maintenance and routine operation of the relevant equipment. The design of the metal ing system, however, must not be left as an afterthought, to be incorporated at a late stage in the final plant design specification. Changes to the design, to accommodate metal ing equipment at this point, are costly and could lead to compromises in the installation of the equipment, which, in turn, could have a negative effect on their operability and long-term reliability. Incorporation of the metal ing system in the plant design specification requires the involvement of the plant operating personnel during the design of the system. This involvement, together with thorough education and training of the operating personnel in the theory and practice of the ing system, are essential for the successful implementation and operation of the system and for the acceptance, by the operating staff, of the system’s principles and procedures. The specification of the metal ing system should comply with this Code and it should include a clear set of objectives, including:

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A system for data acquisition and management

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Reporting intervals, dates and timing

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Samples, sampling frequency, sample preparation procedures and analyses required, as well as cross-reference to the required analytical procedures and target accuracies

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Mass measurement points, types of measuring devices and the required accuracy

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Reporting rules and procedures for all outputs from the ing system

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Levels of authority for approval of the data and, where necessary, for handling and approving changes to the data

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ing battery limits

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Stock-take procedures and frequencies

The major objectives of a metal ing system are to ensure that:

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the production of the metal balance is timely in of required operational decisions, in addition to its basic requirement to contribute to financial reporting

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the financial risk associated with the metal ing system can be quantified and managed

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the entire system is transparent, with a clear audit trail and well-documented standard procedures, in line with international or local standards, to cover mass measurement, sampling, sample preparation, and analysis

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the system incorporates the necessary and appropriate checking and statistical analysis procedures to enable biases in mass measurement, sampling and analysis to be quantified

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the system is audited by a team of suitably qualified persons (both internal and external to the company) at a pre-determined frequency

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clear levels of authority and clear procedures are set down for handling the metal ing data and making any adjustments to the data

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battery limits of all operations covered by the system are clearly identified and that clear ing procedures are laid down and the appropriate sampling and mass measurement facilities required clearly specified

The design of a metal ing system should cater primarily for the needs of the ing process. Data collection and handling must be thought through during the design stage and reporting requirements should form part of the system specification. Where the metal ing data is required for other purposes, such as forecasting or project evaluation, for example, access to and use of the data should be controlled and limited to designated persons. The system output, however, should be friendly and should be able to be easily incorporated into other company management systems, such as financial reports. The key stages in data management are to:

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agree the performance indicators required for the Metal ing System

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decide what data needs to be collected to measure performance against each indicator

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look at the possible sources of data related to each indicator

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decide who has responsibility for collecting what data

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establish a clear timetable for data collection, so that everyone knows what they should do and when they should do it

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standardise data collection so that everyone gathers and reports data in the same way

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agree the appropriate metal balancing procedures, protocols and algorithms

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train and the data collectors so that they are competent and confident

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check or the data that is collected

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set up appropriate and timely communication or reporting systems

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determine the accuracy limits of the data

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analyse the data to self-assessment and continuous improvement

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evaluate performance using the data

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Battery limits should be specified to meet the needs of the metal ing system and they should focus on the inputs to and the outputs from the specified process system. Measurements of internal transfers and recycle loops, within the battery limits, are usually required to meet specific company management or control purposes and they are secondary to the metal ing goals. As such, they should be accommodated within the metal ing system as far as possible, but the system itself should not be compromised to provide for these secondary outputs. Design of the mass measurement and sampling systems should comply with the needs of the metal ing system, as defined by the battery limits. The level of accuracy and sophistication incorporated into the design should also reflect the value of the material being measured or sampled, as well as the risk to the operation in of both financial loss and damage to the company’s reputation caused by erroneous ing. Measurement points, whether of mass or metal content by sampling and analysis, should provide sufficient, redundant data to allow the metal ing balance to be crosschecked, optimised and reconciled. The rules and procedures required for this optimising and reconciliation process must be clearly defined as part of the metal ing system. Several commercial metal ing software packages for the compilation of metal ing data, using a central database, are available. These packages are usually designed to be -friendly, requiring minimal training of metal ing personnel, and they are considered preferable to the use of spreadsheets developed in-house, as these can easily be corrupted and they are seldom adequately documented. However, manual entry of data to operator log sheets and transposition to spreadsheets is common practice on some plants. Spreadsheets do not allow for controlled data entry; it is easy to inadvertently type over data, lose track of records, duplicate and improperly enter data in a spreadsheet. Further to this, the justification of formulae in spreadsheets often lacks clarity, and the justification is not always recorded with the spreadsheet and it can get lost, especially if the developer of the spreadsheet moves on, thereby making spreadsheets vulnerable to error and unjustified computations. For these reasons, spreadsheets must be avoided as a method of data entry.

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11 11.1

METAL ING AUDITING RULES

INTRODUCTION

A company must have metal ing rules and a procedure for auditing these. These rules are primarily technical to provide management information and to promote good corporate governance but they must also provide the quantities and the reconciliation necessary for the financial reports. A set of “Check Lists” is given in Appendix D, which can form the basis of inspections and audits.

11.2

AUDIT TEAM AND ITS RESPONSIBILITIES

A team to audit the metal ing system must be established and it must include a financial auditor. Periodically, the team must include a Competent Person who could be a member of the Company Head Office Internal Audit team, but preferably should be an outsider with no connection to the company. The team’s objective is to obtain a fair and soundly based analysis of an existing metal balancing and reconciliation operation and to report its findings as appropriate. A minimum of an annual stock-take is preferred, both to confirm the base line figures and to lend credibility to the metal s. The team will:

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witness stock-takes

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the mass flow measurements and analytical results

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assess compliance of mass flow, sampling, sample preparation and analytical procedures.

In addition, an assessment of the accuracy, precision and reconciliation of metal ing figures is required. To this end, an audit trail, with clear ing documentation, is essential. Reporting should be done to suit the company’s reporting period, in compliance with international practice and stock exchange requirements, as applicable. The inclusion of an annual stock-take as part of the report is recommended. The use of a Competent Person to sign off the metal s is seen as an issue relating to sound corporate governance, while other critical issues are transparency, credibility and full disclosure. The audit must identify and advise on areas that are inadequate and the recommended actions required to achieve satisfactory performance. It is essential that the Competent Person conducting the audit should have an adequate structure. It should not be necessary for the Competent Person to sign off the metal s every year, but there must be an independent audit of the metal ing procedures by a Competent Person at least every three years.

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11.3

METAL ING REVIEW

On at least an annual basis, the metal ing system methodology must be reviewed by a team of suitably experienced staff, to ensure that it is still fulfilling its requirements. The review should draw information from a number of sources, including:

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Results from internal audits

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External assessments

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Internal quality control studies

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Any ongoing biases indicated in any of the measurements.

Should this review lead to recommendations for changes to the existing system, these must be approved by a Competent Person, prior to incorporation in the system.

11.4

PHYSICAL AUDITS

A series of physical audits must be implemented to ensure that all aspects of the metal ing chain are operating as specified. It is preferable that pre-designed hard-copy checklists for each operation are followed to ensure that the audits are consistent and that they are retained as a record. The recommendations given in the Australian Standard AS 4433.6 should be followed and they can be easily adapted to apply to mass measurement devices as well. For laboratories, the recommendations given in the CITAC/EUROCHEM Guide CG1 must be carried out at least bi-annually. Obviously, the auditing of problem areas must be scheduled at a frequency to enable the problem to be positively identified and eliminated in as short a time period as possible.

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GLOSSARY Acceptance Test – A test carried out on a new conveyor weigher installation in order to determine whether the error is within the specified tolerance. ability – The comparison of the total output of a plant, or section of a plant, to its total input, normally expressed as a percentage. ing Period – The period in which a company would normally report its financial results to the market. For example in the USA this is quarterly, while in many other financial markets the requirement is bi-annually. In metal ing, however, the ing period is usually one month, but metal ing reports also have to be prepared to cover the company’s financial ing period. ing Standards – The financial ing standards required by the relevant regulatory authorities including Stock Exchanges. Accreditation – Procedure by which an authoritative body gives formal recognition that a body or person is competent to carry out specific tasks. Accuracy – A measurement is accurate if it, or the average of a number of measurements, is close to the true value. In metallurgical operations this true value is unknown. In addition, there is often misunderstanding between the accuracy and precision, which is the measure of the spread of a number of measurements around their mean value. For these reasons, it has been decided to adopt the following definition for accuracy, which incorporates the concept of precision, and is based on that given in ISO 5725-1:1994. A measurement that is accurate is one that is free of bias and has a dispersion (standard deviation) that is lower than a defined dispersion or indeed a probability density of a particular nature. The level of dispersion or the nature of the probability density is defined with the purpose of separating measurements that are entirely fit for a particular purpose or use and those that are not. Acid Mine (Rock) Drainage – The low pH effluent which seeps from waste rock, ore dumps or stockpiles of materials which have an insufficient capacity to neutralise the acidic products, containing the dissolution products of minerals. Actual Recovery – The sum of all useful or desired outputs from a process, divided by the sum of all inputs to that process, expressed as a percentage. Adjustments – The changes which are applied to each measurand to achieve a mathematically exact balance, that is a balance where all of the balance flow sheet constraints are satisfied and total and component masses are consistent across the complete balance. Analysis Depth – Is the maximum depth in a sample, such as a pressed pellet, from which the fluorescent X-rays of an element can leave the sample. Analyte – The component of a material whose concentration is determined by chemical analysis. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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Audit Trails – In metal ing, an audit trail is a sequence of paperwork/data entries that validates or invalidates metal ing entries. Many metal ing entries are based on those made to computers, and the term could also be used for an electronic or paper log used to track computer activity. Auditability – A -oriented quality requirement specifying the degree to which an application shall keep sufficient records to a financial or technical audit. Auger Sampler – A tubular device containing an Archimedean screw which when driven through material in a container retains a representative portion of that material. Auger Sampling – An increment is collected from, usually, a relatively fine and dry particulate material using a correctly designed auger sampler. Back-up Systems – Systems for copying or saving metal ing data to a different location so that one can restore the back-up copy if data is lost or damaged. Barren Solution (Tails Solution) – The discard solution from a metal recovery process. Battery Limits – The boundaries of a metal ing system, across which all inputs to and outputs from that system are measured and balanced. Belt Scale/Weigher – A mass measurement device that continuously integrates and records as a cumulative mass, the load on a belt while it es the suspended scale or measuring section of the belt. Belt travel speed – The average speed of a conveyor belt, which is used in conjunction with the weigher, to calculate the tonnage of material conveyed over a period of time. Belt-speed sensor – A device that generates a signal as a result of the belt speed. Bench Scale Plant – Equipment used to perform tests in a laboratory on a relatively small scale, on a stage in a process or the entire process in order to establish the parameters and performance of the process. Berm – An elongated earthen structure which acts as a barrier to contain or redirect the flow of water or solution. Best Practical Means – The most precise method of performing a measurement taking into the risk, degree of importance, cost and practicality for the stream and variable being measured. Best Practices – The most precise method of performing a measurement with the information and equipment available at that time. Bias – Bias is the difference between the mean result of one or more measurements and the true value of the quantity being measured. It also can be seen as an ongoing difference between two, or more, measuring systems. (e.g. sender’s and receiver’s weights, the analysis of the same sample by two laboratories etc.)

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Bin Scale – A bin that is fitted with load sensors or other mass detecting devices, which has been calibrated so that the weight of the contents can be established and weighed on a continuous or batch basis. Bubble Method – A method whereby the flow of material through a process plant is interrupted to allow for equipment to be emptied in series to facilitate stock-takes (normally applied to high value materials in the latter stages of processing). Built-up Recovery – The sum of all useful or desired outputs from a process, divided by the sum of all outputs from that process, expressed as a percentage. Bulk Density – The density of a granular material normally stored in a bin, other container or stockpile, defined as the mass of the material divided by the volume occupied by the material (including the space between the particles). By-products – A metal/commodity made during the manufacture of another. They are incidental to the production of the common products and often contribute a relatively small value to the process revenue. Calibration Test (flow meters) – A set of operations which establish, under specified conditions, the relationship between values of quantities indicated by a flow meter and the corresponding values indicated by a standard of reference. Calibration Test (weighers) – A test, using known weights and forces to load a scale, in order to determine the performance and operating tolerance compared to standard values. Carbon Credits – The process of trading (buying or selling), between countries, operations or companies, an achieved reduction in the emission of green house gases (GHG) such as carbon dioxide, through the mechanisms established for that purpose as a result of the Kyoto Protocol. Certification (Test) – A calibration of a scale or flow meter in of accepted standards defined by the relevant government authority to permit its use for trade purposes and custody transfer. Certified Reference Material (CRM) – Reference material, accompanied by a certificate, one or more of whose property values are certified by a procedure, which establishes its traceability to an accurate realisation of the units in which the property values are expressed, and for which each certified value is accompanied by an uncertainty at a stated level of confidence. Certified Weight – A weight, which has been certified by the relevant national body, or an agent authorised by that body, to have a mass within a defined tolerance, to be used for certifying weighing instruments. Check in-Check out System – The system whereby all streams into and out of the Process or Plant, for which the balance is being performed, are measured, sampled and analysed.

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Closure Errors – The difference between the inputs and outputs for a complete metal balance (that is Check In-Check Out) over a plant or process, including inventory and stock changes, over a prescribed period, and zero. Coefficient of Variation – The quotient of the standard deviation and the mean value of a measurement, expressed as a percentage. Combined Standard Uncertainty – The result of the combination of standard uncertainty components. Common Product – Metals or commodities produced in large quantities, which make a material contribution to process revenue. Competent Person – A person with suitable qualifications and experience to undertake a review of the design and the performance of the Metal ing system and Balance. Concentrate – Term used for the product from a mineral processing operation such as gravity separation or flotation in which the valuable constituents have been upgraded and a large proportion of the gangue material discarded in the tailings. Concentration Ratio – The ratio of the tonnes of ore in the feed to the tonnes produced as concentrate. Consistency Errors – A set of samples, providing an estimate of the value of a parameter, is said to be consistent if, as the number of samples increases, the limiting value of the estimate is the true value of the parameter in question. Constraint – In a mathematical expression or model, a constraint (restriction) occurs for every parameter in that model or expression, which has to be estimated from observed data. (See also Degrees of Freedom). Coriolis Mass Flow Meter – A device, consisting of a flow sensor and a transmitter, which primarily measures the mass flow by means of the interaction between a flowing fluid and the oscillation of a tube or tubes; it may also provide measurements of the density and the process temperature of the fluid. Corporate Governance – Corporate governance is concerned with holding the balance between economic and social goals and between individual and communal goals. The corporate governance framework is there to encourage the efficient use of resources and equally to require ability for the stewardship of those resources. The aim is to align as nearly as possible the interests of individuals, corporations and society. Coupled In-Motion Weighing – The weighing in-motion of a train of coupled wagons to obtain a weight indication or print out of the individual wagons. Coverage Factor – A number that, when multiplied by the combined standard uncertainty, produces an interval (the expanded uncertainty) about the measurement result that may be expected to encom a large, specified fraction (e.g. 95%) of the distribution of values that could be reasonably attributed to the measurand. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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Cross-Stream Sampler – A mechanical sampling device that is used to collect an increment from a falling stream of material either from a conveyor of particulate material or from a slurry stream. Custody Transfer – The transfer of risk and/or ownership of a material from one party to another. This includes ore from a mine to a processing plant, intermediate products such as concentrate and matte from one plant to another, both in-house and inter-company and waste products for sale or safe disposal, as well as final products. CUSUM Graphs – CUSUM (Cumulative Sum) graphs are constructed by plotting the cumulative difference between the measurand and its accepted value. Measurements are “In control” if the plotted line meanders around the zero difference line. Plotted lines that deviate from the zero difference line are an indication of bias. Data – In a metal balancing context is almost always a numerical measurement. (See also Smoothing) Degrees of Freedom – The number of degrees of freedom of a statistic is defined as the number of independent observations in the sample (i.e. the sample size) minus the number of population parameters, which must be estimated from those sample observations. Demonstration Plant – A small-scale plant (but orders of magnitude larger than a pilot plant) designed to test and prove a process and equipment, through a series of stages on a continuous basis, on specific feed materials. Differential Pressure – The difference between the static pressures measured at the wall pressure tappings, one of which is on the upstream side and the other on the downstream side (or in the throat for a Venturi tube) of a primary device inserted in a straight pipe through which flow occurs, when any difference in height between the upstream and downstream tappings has been taken into . Division – The process of decreasing sample mass (without modification of the particle size of the constituent pieces) whereby one or more representative parts of the sample are retained. (AS 4433.2 – 1997) Draught Survey – A mass measurement technique that is based on converting a difference between a vessel’s displacement under different loads into a mass on the basis of its draught tables while taking into the density and temperature of the water and ballast, and changes in ballast and supplies. Dump Leaching – Normally refers to a process whereby metals are extracted by "heaping" uncrushed/broken Run of Mine ore on sloping impermeable pads (with suitable low cost drainage of the leach solutions) and spraying the heaps with a lixiviant, which dissolves the metal content. Sometimes the process is carried out utilising low-grade ore which was previously stockpiled, as treatment through the normal process route was uneconomic. Often the lixiviant used is the barren solution from other primary leach operations. The metal-laden solution is collected for metal recovery.

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Durability error – The difference between the intrinsic error over a period of use and the initial intrinsic error of an instrument. Electromagnetic Flow Meter – A flow meter, which creates a magnetic field perpendicular to the flow, thus enabling the flow rate to be deduced from the induced electromotive force produced by the motion of a conducting fluid in the magnetic field. Electromechanical Belt Scale/Weigher – A mass measurement device whereby a loaded belt is continuously weighed by mechanical means (load cells, levers) and the measurement converted into an electronic signal that can be integrated (totalised) and recorded. Enrichment Ratio – The ratio of the metal grade in the concentrate to the metal grade in the feed. Equiprobable Opportunity – The requirement that every particle in a process stream has an equal opportunity of being collected as part of a sample. Error of Measurement – The result of a measurement minus the value of the measurand (not precisely quantifiable because the true value lies somewhere unknown within the range of uncertainty). Estimated Recovery – Where redundant data are available, the estimated recovery will depend on which measurement points are chosen as the basis of the calculation. For example, the mass and grade of the saleable product is usually accepted as the value agreed as part of a commercial transaction. If the mass and grade of the feed to the process are known from measurement and sampling, an “input recovery” may be calculated. Evaluation – The process by which the data required for Metal ing is acquired. Exception Report – A report generated, in of this Code, whereby each noncompliance with the requirements of this Code is motivated and approved. Expanded Uncertainty – Obtained by multiplying the combined standard uncertainty by a coverage factor. Flexible Hose Sampler – A sampler where the material to be sampled is confined within a hose which can be mechanically moved across a stationary sample cutter. Gamma Belt Weighers – See Nuclear Belt Weigher. Gantry scale – A scale where the load measuring elements are attached to a gantry crane normally used for loading or offloading materials in bulk. Hazops (Hazard and Operability Studies) - A process conducted during process design in which the plant design is reviewed to eliminate or reduce hazards and reduce operating difficulties. Heap Leaching – Normally refers to the leaching of ores that have been crushed (to an economic size) prior to placement on an impermeable pad on which suitable AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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drainage lines have been placed for the collection of lixiviant that has been percolated through the crushed and prepared ore. The crush size selected may vary considerably but typically ranges between 12 and 50 mm. Home and Away System (Double Sampling and Analysis) – A system where batches/lots are weighed and sampled by both the sender and receiver. Data from the analysis of the samples can then be statistically analysed to establish the presence of biases in the sampling and/or analytical processes. In the “Half Home and Away System” samples from even numbered batches of material are analysed by the sampling sites laboratory (i.e. sender at sender’s laboratory and receiver at receiver’s laboratory) and odd numbered batches are analysed at the other laboratory (i.e. sender at receiver’s laboratory and receiver at sender’s laboratory). Statistical analysis of the data can then establish the level of bias in either the sampling or analytical procedures. Hopper Scale – A scale designed for the weighing of granular materials in bulk, whose load-receiving element is a self-cleaning hopper with an outlet gate. Increment – A quantity of material collected in a single operation of a sampling device. (AS 4433.2 – 1997) Initial intrinsic error – The intrinsic error of an instrument as determined prior to performance tests and durability evaluations. In-motion weighing – The weighing of objects that are in-motion. Usually applied to rail or road trucks. In-process Inventory – The quantity of material contained in a process plant through the various stages from the initial feed to the plant to the production of product(s) from the plant. It does not include stocks of feed material or products which are defined as Plant Stocks. In-situ Leaching – A process whereby the metal contained in an ore body is leached in-situ by pumping a lixiviant into the ore, which is normally naturally fractured or has been broken by explosives and the pregnant solution withdrawn Inter-leaved Experiments – To carry out interleaved sampling, a sampling system must be capable of taking increments at twice the usual rate. Odd-numbered increments are collected into one sample and the even-numbered ones are collected into a second sample over the sampling period of interest. Both samples are, at the end of the sampling period, nominally identical. If they are both separately prepared and analysed in the same way, the difference between the results in an indication of the precision of sampling. A substantial number of results from interleaved pairs of samples can be accumulated over a period of time to arrive at a good estimate of the average sampling precision. It is also possible to draw certain conclusions regarding the distribution of the assay uncertainties. Internal Working Reference Material (IWRM) – A carefully prepared, homogeneous material whose assigned values have been obtained from a round-robin exercise using accredited methods of analysis and by comparison to CRMs.

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International Unit of Mass – This is defined as the kilogram. The definitive standard weight is held at the International Bureau of Weights and Measures in , which is also responsible for certifying National standards such as the National Prototype Kilogram. Intrinsic error – The error of an instrument under reference conditions. Isokinetic Sampling – A sampling method, normally used for gas streams containing dust, or effluent streams containing solids, whereby the sample is withdrawn from a conduit, duct or stack in a manner that withdraws the sample across the diameter of the conduit, so in such a way that there is no change in the velocity of the sample stream as it enters the sampler. Also used for personal sampling of ambient air. K Factor (Belt Weighers) – The ratio of the result of the mass determined by a bulk test, arrived at by ing a known weight of material over the weigher, as compared to that obtained by the use of test weights. Kyoto Protocol – A Protocol arising from the meeting of 160 nations, in of the International Convention for Climate Change, which took place in Kyoto, Japan in 1997, whereby signatory countries agreed to reduce the emission of greenhouse gases by certain percentages, relative to levels pertaining in 1990 during the period 2008 - 2012. It came into force on the 16th February 2005. Level of Confidence – The probability that the value of the measurand lies within the quoted range of uncertainty. LIMS – Acronym for Laboratory Information Management System, a computer system for gathering and analysing laboratory information. These systems are used for:

•

Automatic data collection. The LIMS may be interfaced directly to the laboratory instruments, offering rapid data collection, while eliminating the risk of transcription errors.

•

Provision of electronic records of laboratory results. Electronic records offer many benefits, such as the ability to link data together, search, retrieve and report rapidly and backup and disseminate data easily through an operation.

•

Provision of a structured repository for laboratory data

Lixiviant – A solution containing acids, other chemicals, or bacteria used to dissolve metals or metal compounds. Load Cell (Sensor) – A device which produces an output signal proportional to the applied weight or load. The load cell may utilise any physical principle included in the field of, but not limited to, electricity, electronics, hydraulics, magnetics, mechanics and pneumatics or combinations thereof. Lot – A lot is a defined quantity of the material being measured to which the mass and analysis is applicable. It may be a shipment, a production batch or production over a defined time period, such as a shift or a day, or a fixed tonnage of material. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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For ing purposes, a weighted average sample of all the production or shipment batches making up that lot is prepared and submitted for analysis. Lot/Batch – A certain quantity of material for determination of its average quality. (See also Lot in the Glossary) Mass Balancing – The process whereby input and process streams, or the individual components of the streams, stocks and in-process inventory are measured, sampled and analysed in order to calculate the mass of each and reconcile the output to the input, plus or minus the stock change. Materiality – It is what affects business decisions at the top level of management. It is not a formally defined value but should be determined by senior management for each company and/or operation. For metal ing, a measurement for a process flow or in-process inventory may be regarded as material if:

•

The metal content is significant in value and/or

•

The content is regarded as important with respect to the company’s or operation’s risk profile, public perceptions or reputation and/or

•

It will reflect on the balance sheet and financial statements

Material-run (bulk) Test – A method of calibrating a belt scale whereby a known weight of the material (either pre or post weighed on a test scale) being weighed is ed on the conveyor through the mass detection system. Mathematically Exact Balance – A mathematically exact balance means a balance where all of the balance flowsheet constraints are satisfied and total and component masses are consistent across the complete balance with no uned losses or gains. Matte – The upgraded product from a smelting furnace or converting operation for sulphidic ores or concentrates which contains the majority of the metal values and a lower proportion of gangue materials (a large proportion of which have been removed in the slag). Furnace (green) matte still contains a high proportion of iron whilst in converter (white) matte the iron content is much lower and the value metals higher. Maximum Permissible Tolerance (Error) – The maximum difference, positive or negative, permitted between the indication of an instrument and the corresponding true value, as determined by verification standard masses, with the instrument being at zero at no-load, in the reference position. Mean (of data) – The mean of a set of n items of data is the arithmetic average of the series of measurements xi. The mean is usually designated by x with a bar above it. Measurand – The specific quantity subject to measurement. Mechanically Correct Sampling – Correct sampling of a particulate material from a moving stream is achieved when the expected number fraction of the particles in the sample is equal to the average number fraction of the particles in the process stream during the time that it takes to extract the sample increment. To achieve this AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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objective, the sampler must be correctly designed, installed, maintained and operated. A sampler that is mechanically correct is unbiased. Where possible, the correctness of the sampler and its freedom from a specified level of bias should be confirmed by carrying out bias tests by the stopped belt method or other suitable reference method of sampling. Metadata – The term used to describe data about data. It describes who collects the data, what the data contains, where (and how) the data is stored, when (and how often), and why the data is collected, Metal ing – The system whereby selected process data (pertaining to metals of economic interest) is collected from various sources including mass measurement and analysis and transformed into a coherent report format that is delivered in a timely fashion in order to meet specified reporting requirements. Metallurgical (Metal) Balance – The determination through measurement, analysis and computation of the magnitude of each component of interest within each section of a process flow sheet. A metallurgical balance is commonly referred to as a “metal balance” or as a “metbalance”. The latter is preferred as the process is applicable to any mineral based commodity – it is not restricted to metallic elements. A metallurgical balance, which is suitable for reporting of metallurgical performance, should be supplemented by defined errors associated with each determination. Metrology – The science of Weights and Measures. Microwave Gauge – An instrument that utilises microwaves to measure the moisture content associated with a material. Mine Call Factor – The ratio, expressed as a percentage, which the specific product ed for in the product recovered, plus that in tailings/residues, bears to the corresponding product called for by the mine’s measuring and evaluation methods. It is also the product of the Shaft and Plant Call Factors. Momentum Mass Flow Meter (Impact Mass Flow Meter) – A device that detects mass flow rate by measuring the momentum of a stream of material falling a definite distance under gravity. National Prototype Kilogram – The standard certified weight which is held by individual countries and which has been certified by the International Bureau of Weights and Measures. This standard weight is used to certify, by the national Metrology organisation, the test weights used to certify all weighing equipment. Neutron Gauge – A nuclear gauge that utilises an emission of neutrons to effect the measurement. Nominal Top Size – The mesh size that will allow 95% of the sample to through. Nomograph – A graphical presentation of related sets of data enabling specific experimental conditions to be pre-determined, such as in the use of Sampling Safety Line. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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Normal Distribution – A continuous distribution of a random variable, with mean µ 2 and variance σ and probability density:

p(x) =

⎛ ( x - µ )2 exp ⎜ ⎜ 2σ 2 2πσ ⎝ 1

⎞ ⎟ ⎟ ⎠

Nuclear Belt Scale/Weigher – A mass measurement device that continuously integrates and records as a cumulative mass the load on a belt while it es the nuclear source by measuring the absorption of the gamma rays by the material on the belt. Nuclear Gauge – An instrument that emits a source of neutrons or radiation (usually gamma rays) which through the material in a conduit, on a belt, or in a stockpile or bin, and has a receptor that collects the radiation on the other side, thus enabling a measurement of the adsorption and hence the moisture content or density of the material. P & ID – Process and Instrumentation Diagram. Paired t-test – A single sample t-test appropriate for comparing two samples in which individual are paired, and observed values within pairs are positively correlated. Penetrometer – An instrument that measures in-situ density by measuring the backpressure resulting from the penetration of a cone into the material being measured. Usually applied to soils but is also utilised for stockpiles of material such as coal of a relatively fine uniform size. Permanent Inventory – In-process Inventory that has been absorbed into the fabric of the processing plant, such as gold into concrete foundations, which is only recovered when the plant is demolished at the end of it’s life. PFD – Process Flow Diagram. PGMs – Platinum Group Metals comprising of platinum, palladium, rhodium, iridium, ruthenium and osmium. Photogrammetry – The process of producing 3-D images by multiple photographs of an object. Pilot Plant – A small-scale plant to test a process or process stages on a specific process, on a continuous basis. Plant Call Factor – The mass of a species recovered to product and to tailings divided by the mass of species fed to the plant, expressed as a percentage. Plant Historian – A computer system for accurate operational data archiving and its rapid recovery. The data historian accepts data input only, does not recognise structures and procedures, and is strictly concerned with the accurate, efficient data storage and recovery if required. Data historians perform three key functions:

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•

Data compression

•

Rapid data recovery

•

Data save-set facility

Plant Inventory – The inventory can be stockpiles of feed, intermediates or products (plant stocks) as well as transient and long-term in-process inventory. Plant Recovery – The quantity of metal in the product stream from a plant, divided by the quantity of metal in the feed to that plant, expressed as a percentage. Plant Stocks – Any material received by a plant, from a previous processing operation, but which has not yet entered a process stream, or any material produced by that plant to dispatch standard quality, but which has not yet been dispatched. Platform Scale – A compound lever scale whose load receiving element is a platform that is ed by four or (rarely) more main bearings. Precision – Of a measurement depends on the closeness of the outcomes of a repeated measurement or test procedure. Hence it depends only on the distribution of random errors and not on any relationship to a “true” value. It is usually expressed as the standard deviation of the test results. That is, by a measure of imprecision. Precise measurements have a dispersion about their mean value, which is lower than a defined dispersion or indeed a probability density of a particular nature. The level of dispersion or the nature of the probability density is defined with the purpose of separating measurements that are entirely fit for a particular purpose or use and those that are not. A precise measurement may not be accurate; its mean may differ from the true value of the measured quantity by an arbitrary amount. Standards often quantify the term precision as a value corresponding to the magnitude of a 95% confidence interval around a result. If the n measurements of the quantity to be estimated are normally distributed, the interval is ± ts, where t is the two-sided Student-t value at 95% confidence and n-1 degrees of freedom and s is the estimate of the standard deviation of the estimated quantity. Pregnant Solution – A solution containing dissolved metals, (e.g. from a leaching operation) which is normally the feed to further processing to extract the product metals. Primary ing – The metal balance across an entire plant as determined by Check in-Check out. Propagated Error – The error in succeeding steps of a process due to the occurrence of an earlier error. Quality – Degree to which a set of inherent characteristics fulfils requirements. Quality Assurance (QA) – QA describes the overall measures that a laboratory uses to ensure the quality of its operations. Quality Control (QC) – The operational techniques and activities that are used to fulfil the requirements for quality. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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Random Error – The component of the error of measurement, which in the course of a number of measurements of the same measurand, varies in an unpredictable way. Random Stratified Sampling – The taking of increments whereby the material being sampled is divided into strata, one increment being taken at random within each stratum. (AS 4433.1 – 1997) Reconciliation – A metallurgical balance which relates production of saleable and reject or waste materials from a process back to its source as ore or other feed material. It should be provided with defined and stated errors as for any other metallurgical balance. Reconstitution – If the mass and grade of the process rejects are known from measurement and sampling, then an estimate of the feed to the process can be made by adding together product and waste. A recovery based on this calculation is often referred to as an output recovery. Given that all measurements (mass, sampling and analysis) are subject to random errors (and possibly biases) the numerical values of input and output recovery are not expected to be equal although they should agree to within the errors of measurement imposed on each calculation process. That is, they may be statistically identical. Recoverable Metal – The proportion of a metal in a feed material that has been determined to be recoverable, by laboratory and/or pilot plant tests, by the chosen process, with the remaining metal being defined as Non Recoverable. Recovery – Of a particular component is defined as the proportion of that component in the feed source, which is estimated to have reported to each process stream but most commonly to the saleable product. Hence, recovery is a measure of performance efficiency and is always a key performance indicator. Reduction – The process of decreasing the dimension of pieces constituting the sample, without modification of mass of the sample or of its composition. (AS 4433.2 – 1997) Reference Material (RM) – Material or substance one or more of whose property values are sufficiently homogeneous and well established to be used for the calibration of an apparatus, the assessment of a measurement method, or for asg values to a material. Relative Density – The relative density of a substance is the ratio of the density of the substance at a specified temperature to the density of a reference substance at the same or a specified different temperature (usually the reference substance is water at a temperature of 4 ºC). Relative density is a dimensionless quantity and is also referred to as specific gravity. Relative Standard Deviation – Relative standard deviation is the standard deviation of a measurement, normally expressed as a percentage of the mean of that measurement.

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Repeatability – Repeatability is a measure of the extent to which measurements made in the same laboratory with the same instrument will be similar. Australian Standards define the repeatability of a measurement as the maximum acceptable difference between duplicate determinations carried out in the same laboratory on the same sample. If the absolute value of the difference between the two determinations exceeds the repeatability, the duplicate assay must be carried out again. Repeatability of a measuring instrument is the quantity that characterises the ability of the measuring instrument to give identical indications or responses for repeated applications of the same value of the quantity measured under stated conditions of use. Repeatability of a measurement is a quantitative expression of the closeness of agreement between successive measurements of the same value of the same quantity carried out by the same method with the same measuring instrument at the same location at appropriately short intervals of time. Repeatability (in Mass Measurement) – The capability of producing anew a previous indication when the previously applied loads or forces are again applied. Representative – A sample can be said to be representative of the lot from which it was taken if the mean square of the total sampling accuracy (uncertainty) is less than some standard of representativeness. The accuracy includes both the bias and the precision and its mean square has the expected value of σ2 + b2, where σ if the standard deviation characterising the precision and b is the bias. The standard of representativeness is a value chosen by the to suit the circumstances and the use to which the numbers are to be put. Reproducibility – Reproducibility is a measure of the extent to which measurements made in different laboratories will be similar. Australian Standards define the reproducibility of a measurement as the maximum acceptable difference between the mean of duplicate determinations carried out in one laboratory and the mean of duplicate determinations carried out in a second laboratory on representative samples taken from the same bulk sample, after the last stage of the reduction process. Reproducibility (in Mass Measurement) – The measure of the extent that weights determined by two different instruments or methods will be similar. Restore Methods – Methods for the restoration of electronic data or stored documents from back-up media at the same or an alternate location, normally only required in the event of a disaster. Risk – The chance of an event occurring that will have an impact on objectives. A risk may be quantifiable in of the likelihood of loss, less than expected returns or an undesirable outcome. (The VALMIN Code, 2005 Edition). Risk Assessment – A formal process for the identification and quantification of the risks associated with the all the processes involved in metal ing.

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Round Robin (Proficiency Testing) – An exercise where sub-samples of the same material are circulated to a number of laboratories for analysis. The results are used to establish an accepted value for analytes of interest or to measure the bias of a laboratory from an already known accepted value. Run of Mine (ROM) Ore – Ore produced from a mining operation, which has not been subject to any size reduction or other processing. Sample – A subset of a population chosen in such a way that it can be taken to represent the population with respect to some characteristic. Sampling Safety Line – Is usually constructed in a nomograph and relates the minimum sample mass required at a particular particle size to achieve a specified level of variance. Gy introduced the concept of a 'safety line', which is based on the relationship between the introduced variance and the particle top size. If the sampling constant is taken to be dependent only on particle top size and the sampling constant is known at one particular state of comminution of the material, a 'sampling safety line' can be drawn for the particular analyte to which the sampling constant applies. Sampling Constant – The sampling constant for a material describes its heterogeneity with respect to a particular analyte. The sampling constant is usually proportional to the cube of the 95% ing size of the particles in the sample, but this is not always true. Important exceptions to this rule occur when sampling materials that are low-grade overall but in which the valuable component occurs as relatively large blebs or nuggets of high-grade material. Diamond ores, alluvial gold and PGM ore containing 'metallics' where the valuable component is present at g/tonne levels are the most notable exceptions. Low-grade materials with small inclusions of high-grade material can have very large sampling constants. The sampling constant has units of mass, usually expressed in grams. SCADA – Acronym for Supervisory Control And Data Acquisition, a computer system for gathering and analysing real time plant data. SCADA systems are used in industry to monitor and control plant status and provide logging facilities. As the name indicates, it is not a full control system, but rather focuses on the supervisory level. As such, it is a purely software package that is positioned on top of hardware to which it is interfaced, in general via Programmable Logic Controllers (PLCs), or other commercial hardware modules. Secondary ing – Conducting mass balances over smaller sections of the total plant circuit, to identify where lock-ups are occurring, time lags are involved or where any measurement problems exist. Semi-Permanent Inventory – In-process Inventory contained in process equipment such as mills (including the liners) or furnaces (including the refractories) that is only recovered periodically when the equipment is maintained or refurbished. Sensitivity (as applied to a scale) – The responsiveness of a weighing element or indicator to a change in the applied load or the ratio of the output response to a

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specified change in the input. measuring chain.

The latter can be applied to any element in the

Shaft (or Mine Section) Call Factor – The mass of a species delivered to a shaft compared to the mass of that species calculated from sampling and surveying procedures, expressed as a percentage. Shewhart (Control) Graphs – These graphs are constructed by plotting the value of a measurand about a central line of its accepted value. Control and Action lines are usually drawn at ±2 and ±3 standard deviation levels respectively. Measurements are said to be “In control” if they are within the 2 standard deviation limits, but “Action” is required if they are outside 3 standard deviations. Shipments – An amount of material transported as a defined entity such as a truck, rail car or ship. It may contain several lots. Slag – The product from a furnace, smelting or converting operation which normally contains the gangue materials such as silica, alumina and magnesia in oxide form and small quantities of the value metals, which is discarded or recycled. An exception is the titania slag from the smelting of ilmenite which is a raw material for titanium oxide production and is sold. Smoothing – The process of removing or reducing fluctuations in data in order to produce a result that is visually smooth. Typically this is performed by some form of local averaging. Sonic Drilling – A technique for drilling that utilises high frequency vibrations generated from sound waves to penetrate the material being drilled and generate core samples. Sonic Drilling – A technique for drilling that utilises high frequency vibrations generated from sound waves to penetrate the material being drilled and generate core samples. Specific Gravity – See relative density. Splitting Limits – The limits within which values from procedures such as twinstream analysis or inter-laboratory analysis should agree. Spurious Error – The error which invalidates a measurement. They generally have a single cause such as the incorrect reading of one or more significant digits or the malfunction of instruments. Standard Deviation (SD) – The square root of the variance. Standard Uncertainty – The estimated standard deviation. Static Scale – A mass measurement device that converts a static load on a weighbridge or on a platform, inside a hopper or suspended from a gantry scale into a mass.

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Stopped Belt Sampling – An increment of particulate material is manually removed from a conveyor belt using a correctly designed former. Strata – Approximately equal parts of a lot or sub lot based on intervals of time, mass or space. (AS 4433.1 – 1997) Systematic Error – The component of the error of measurement which, in the course of a number of measurements of the same measurand, remains constant or varies in a predictable way. Tailings (Rejects, Residues) – The material after the extraction of the maximum economic amount of the value metals from the feed material such as ore, concentrate or matte, by treatment processes such as concentration or leaching. Tare – The mass of an empty container such as a drum, bin, or road or rail truck. Test chains – A calibrating device of known certified weight consisting of a series of rollers or wheels linked together to ensure their weight is uniform and they can move freely (so that chain weight loss due to wear is minimised). Theoretical Recovery – The ratio of the metal input to a plant, minus the measured losses from that plant, divided by the metal input to the plant, expressed as a percentage. Three-Product Formula – Used in concentrators which produce more than one concentrate product (eg: lead – zinc concentrators), to obtain a metallurgical balance for process monitoring purposes, where the component analyses of the feed and each product stream are known, as well as the mass flow of one of the streams either entering or leaving the plant. Tolerance – The limit of error or variance established by authority or usage or a value fixing the limit of allowable error or departure from true performance or value. (NIST Handbook) Toll Treatment – The treatment, through a metallurgical plant, of material that has been procured from third parties for processing under agreed contractual conditions. The may include the charges for the treatment, recovery percentages for the products, the time frame for production and penalties for deleterious impurities. Totaliser – A device for counting and indicating or printing the algebraic sum of successive inputs made on a serial entry basis. Inputs may represent counts, shaft rotations etc. Traceability – The property of the result of a measurement or the value of a standard whereby it can be related to stated references, usually national or international standards, through an unbroken chain of comparisons, all having stated uncertainties. Transducer – A device for converting energy from one form to another for information or control purposes.

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Transient Process Inventory – Process inventory in process equipment such as tanks and bins that is in a transient state and is moving through the equipment and process on a continuous or regular batch basis. Transparency – The method of measurement and analysis should be fully documented. Triple bottom line reporting – The triple bottom line (TBL) focuses corporations not just on the economic value that they add, but also on the environmental and social value that they add and destroy. At its narrowest, the term 'triple bottom line' is used as a framework for measuring and reporting corporate performance against economic, social and environmental parameters. At its broadest, the term is used to capture the whole set of values, issues and processes that companies must address in order to minimize any harm resulting from their activities and to create economic, social and environmental value. This involves being clear about the company's purpose and taking into consideration the needs of all the company's stakeholders - shareholders, customers, employees, business partners, governments, local communities and the public. t-test (Student’s t test) – A test used on the means of small random samples of one of the following null hypotheses: a

That the sample has been drawn from a population with a given mean

b

That two samples have both been drawn from populations with equal means

The test compares two sets of results and establishes whether or not there are statistically significant differences between them. Two-product formula – Used in concentrators with minimal accumulation of inventory to calculate plant recovery, where: Plant Recovery = (Cc/Ff) x 100% = 100c(f – t)/f(c – t) And C and F are respectively concentrate and feed tonnages And c, f, and t are respectively concentrate, feed and tails grades. Ultrasonic Transit Time Flow Meters – A flow meter utilising the principle that the propagation velocity of sound waves within a fluid is directly influenced by the velocity of that fluid. Umpire Analyses – The analytical results obtained by an Umpire Laboratory. Umpire Laboratory – A laboratory chosen, because of its expertise, to which samples with disputed results can be sent for arbitration. Uned Loss or Gain (Metal ing Variance) – Any discrepancy between actual recovery and the ability over an ing period.

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Uncertainty – A parameter, associated with the result of a measurement that characterises the dispersion of the values that could reasonably be attributed to the measurand. Uncoupled In-Motion Weighing – The weighing in-motion of wagons that travel independently across a load receptor (usually achieved by means of an incline of the approach to the load receptor). Valuation – The assignment of monetary values to the outputs of metal balances, as a separate function from metal ing. Variance – The sum of the squares of the differences between each measured value and the mean of the measured values divided by one less than the number of measurements in the data set. This formula applies only when the individual measurements are uncorrelated. Successive measurements during a day or several days in a plant are often correlated, in which case a different formula must be employed. Weigh Carriage – A structure ing the weigh idlers on a conveyor belt weigher, which in turn transmits the weight through the carriage to the load sensor. Weigh Idlers – Idlers positioned in the weigh carriage assembly so that they sense the weight of material on the conveyor and transmit the weight through the carriage to the load sensor. Weighbridges – Weigh scales designed to weigh road trucks or rail wagons where the lever system is usually below ground in a pit so that the vehicle or rail wagon can over and stop on the scale. In a large capacity scale also refers to the structural frame carried by the main bearing which s the load-receiving element. Y-blenders – Mechanical mixing devices that are used to mix samples within enclosed containers. Yield – The quantity of coal (of the quality that one requires) obtained from Run Of Mine Material expressed as a percentage. There are two subsets: 1

Theoretical Yield: obtained in a laboratory from washability curves. It is higher than practical yield.

2

Practical Yield: obtained on a plant after losses due to process inefficiency.

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LIST OF ACRONYMS AND ABBREVIATIONS AS

Standards Association of Australia

ASME

The American Society of Mechanical Engineers

ASTM

American Society for Testing and Materials

BSI

British Standards Institute

CITAC

Cooperation on International Traceability in Analytical Chemistry

Eurachem Focus for Analytical Chemistry in Europe ECE

Economic Commission for Europe

EPA

Environmental Protection Agency

IEC

International Electrotechnical Commission

IFRS

International Financial Review Society

ILAC

International Laboratory Accreditation Conference (USA)

IPA

International Petroleum Association

ISA

International Society for Automation

ISO

The International Organisation for Standardisation

JIS

Japanese Industrial Standard (Japanese Standards Association)

NATA

National Association of Testing Authorities (Australia)

NISA

National Industrial Scale Association (USA)

NIOSH

National Institute for Occupational Safety and Health

NIST

National Institute of Standards and Technology (USA)

OIML

International Organisation of Legal Metrology

SABS

The South African Bureau of Standards

SAIMM

South African Institute for Mining and Metallurgy

SANAS

South African National Accreditation Service

UKAS

United Kingdom Accreditation Service

UN

United Nations

VAM

Valid Analytical Measurement

VIM

International Vocabulary of Basic and General in Metrology

VML

Vocabulary of Legal Metrology

WHO

World Health Organisation

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APPENDICES APPENDIX A – ACKNOWLEDGEMENTS Contributions from the following companies and/or individuals by the provision of information and to the compilation of this Code document are gratefully acknowledged: AMIRA International Anglo American Corporation Anglo Platinum Corporation Anglo Research Anglo Coal South Africa Anglogold - Ashanti Anglo Technical Division BHP Billiton Endress + Ha (Pty) Limited Impala Platinum Limited JKMRC at the University of Queensland, Australia Namakwa Sands Northparkes Mines Process Automation (Pty) Limited Rio Tinto SA Automation System (Pty) Limited Zincor – A Division of Kumba Base Metals Ltd

The Research Teams: University of Cape Town: Drs Dave Deglon and Aninda Chakraborty, Omari Lukwichi, Paul Bepswa and Erica van Tonder University of Stellenbosch: Dr Jacques Eksteen, Chris Cutler, A Nesbitt, Busisiwe Madonsela, Elton Thyse and Makiwe Nkohla University of Queensland: Dr Rob Morrison, Will Jansen

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P754 Project Management and Coordination: Richard Beck, AMIRA International Development of the original framework for the Code and contributions to the section on Metal Balancing: Dr Rob Morrison, University of Queensland Statistical and Mathematical Review of Code and the section on Statistical Theory of Sampling: Dr Geoff Lyman, Sampling Consultant, Brisbane, Queensland, Australia (for Anglo Platinum) The Code Team: Peter Gaylard, University of Cape Town Ian Ralston, Anglo Technical Division Neville Randolph, NSR Investments CC Mike Wortley, Consultant Review of and comments on Code and Guidelines Release 2: Professor Tim Napier-Munn, University of Queensland Dr Hugh Bartlett, Hugh Bartlett Consulting cc Dr Ralph Holmes, CSIRO Editorial preparation of Release 3: Sally Burt, University of Cape Town Permission to re-publish: Lyman, G J, 2005. Sampling, Metallurgical ing and Reduction of Balance Estimation Variance, in Proceedings Second World Conference on Sampling and Blending, pp 127–135 (The Australasian Institute of Mining and Metallurgy: Melbourne). Re-published with the permission of The Australasian Institute of Mining and Metallurgy. Cutler, C J, and Eksteen, J J, 2006. Variance Propagation in Toll Smelting Operations Treating Multiple Concentrate Stockpiles, JSAIMM vol.106, pp 222–227. Re-published with the permission of the Southern African Institute of Mining and Metallurgy.

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APPENDIX B – REFERENCES B.1

MASS MEASUREMENT

Code and Guidelines: Section 3 1

Colijn, H. (1983). Weighing and Proportioning of Bulk Solids, Trans Tech Publications,

2

ibid, Chapter 7, pp 207-219

3

ibid, Chapter 6, pp 175-193

4

ibid, Chapter 5, pp 145-173

5

ibid, Chapter 6, pp 194-204

6

ibid, Chapter 7, pp 207-226

7

ibid, Chapter 7, pp 226-238

8

ibid, Chapter 7, pp 214-215

9

ibid, Chapter 10, pp 317-328

10 ibid, Chapter 9, pp 313-314 11 ibid, Chapter 2, pp 32-42 12 Crabtree, M. (2000). Flow Handbook, Crown Publications, South Africa 13 Furness, R.A., (1996). Outstanding and Contentious issues in Flow Measurement and Recent Real Advances in Magnetic Flowmeters, ABB Kent Measurement (Pty) Ltd. 14 Merks, J.W., (1985). Sampling and Weighing of Bulk Solids, Trans Tech Publications, D-3392 Clausthal-Zellerfeld, . 15 ibid, Chapter 8 pp. 304-307 16 16. National Industrial Scale Association, “Back Publications of the National Industrial Scale Association” 17 Strauss, T., (2004), Flow Handbook, Endress+Ha Flowtec AG, Switzerland 18 ibid, Chapter 3, pp 50 – 51 19 ibid, Chapter 5, pp 319 – 322 20 ibid, Chapter 11, pp 407 – 410 21 ibid, Chapter 7, pp 292 – 293 22 Merks, J.W. (1988), “Sampling and Weighing of Mineral Concentrates”, Bulk Solids Handling, 8(2), pp. 179-187 23 Colijn, H. (1991), “Weighing of Bulk Solids”, Bulk Solids Handling, 11(1), pp. 113-119 24 Schmedt, R. (1992),”Measurement of Bulk Solids Flow”, Bulk Solids Handling, 4(1), pp.81-84

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25 Boeck, Th. (1987), “Mass Flow Measuring System for Pneumatically Conveyed Solids”, Bulk Solids Handling, 7(4), pp.539-545 26 Kochsiek, Glaser, (2000), “Comprehensive Mass Metrology,” Wiley VCH, Section 3.6 “Determination of Mass in Practice” 27 Liptak, B.G., “Instrument Engineers’ Handbook, Volume 1, Fourth Edition: Process Measurement and Analysis” 28 Johnson, G., “Slurry Flow Measurement”, Paterson and Cooke Consulting Engineers (Pty) Ltd 29 Pomroy, J., “Selecting the Right Flowmeter”, Chemical Engineering Vol.103, No5, May 1996, pp.96–102 30 Brown, N.P, Heywood N.I., “The Right Instrumentation for Slurries”, Chemical Engineering, September 1992, pp.106-112 31 Moore, A., “Selecting a Flowmeter”, The Chemical Engineer, April 1986, pp. 39-45 32 Merret, J., “Go with the Flow”, The Chemical Engineer, May 1996, pp.1922 33 Stumm, Dr. W., “Mass Flow: Part 1 Principles and Techniques”, Pulse Vol. 7 1989, pp. 45-51 34 Stumm, Dr. W., “Mass Flow: Part 2 Coriolis Measuring Characteristics”, Pulse Vol 8 1989, pp. 44-48 35 Hall, J., “Improve plant efficiency with direct mass flow measurements”, The Industrial and Process Control Magazine, Feb. 1983, pp 43-47 36 Labs, W., “Flowmeters: Accuracies are better, prices lower”, The Industrial and Process Control Magazine, Feb. 1990, pp. 29-33 37 Benard, C., “ Flow Measurement in the Power Industry”, Power International, April 1986, pp. 86-89 38 Yan, Y., “Flow Rate Measurement of Bulk solids in Pneumatic Pipelines; Problems and Solutions”, Bulk Solids Handling, Vol 15,1995, pp.447456 39 Merks, J.W. and Merks E.A.T., Precision and Bias for Mass Measurement Techniques, Matrix Consultants, Vancouver, 1992

B.2

MASS FLOW ON CONVEYORS

Guidelines: Section 3.4.3 1

National Industrial Scale Association, (Updated 1998) “Overview of Conveyor Belt Scales”

2

Soderholm, A. (2000), “Conveyor Belt Scales – A Mechanical Dilemma”, Bulk Solids Handling, 20(4), pp 534-537

3

Lockwood, F. (1981), “Continuous Belt Weighers”, Bulk Solids Handling, 1(2), pp. 235-237

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4

Allenberg, B. (1993), “Requirements on Continuous Weighers in a Quality Assurance System”, Bulk Solids Handling, 13(2), pp. 314-318

5

Carpentier, P.T. (1983), “Conveyor Belt Certification: Weighing the various Systems”, Coal Mining and Processing, November pp. 42-48

6

Abbott, J.A. (1989), “The Effect of Conveyor Belt Mistracking on Belt Weighing Errors”, Bulk Solids Handling, 9(1), pp. 107-117

7

Abbott, J.A. (1986), “ The Effect of Idler Misalignment and Belt Stiffness on Belt Weighing Errors”, Bulk Solids Handling, 6(1), pp. 121-128

8

Fristedt, K. (1986), “Belt Weighing Errors – From where do they Origin”, Bulk Solids Handling, 6(5), pp. 963-968

9

Jost, G. (1986), “Comparison of Nuclear and Gravimetric Systems for the Continuous Weighing of Bulk solid Flows”, Bulk Solids Handling, 6(6), pp. 1191-1197

10 Lauber, F.R. (1990), “Alternative Weighing Technology for Belt Conveying Systems”, Bulk Solids Handling, 10(3), pp. 333-335

B.3

BULK MASS MEASUREMENT: DRAUGHT SURVEYS

Code: Section 3.3 and Guidelines: Section 3.4.6 1

Beemster, J. (1992), “A critical look at the draft survey”, Bulk Solids Handling, 12(2), pp. 264-265

2

Bhandary, N.S.C. (1992), “On draft surveys”, Bulk Solids Handling, 12(2), pp. 267

3

Kelly, J.P. (1991), “Trade by draft survey or by belt weighing”, Bulk Solids Handling, 11(1), pp. 239-241

4

Kelly, J.P. (1992), “Draft survey versus legal weighing”, Bulk Solids Handling, 12(2), pp. 259-260

5

Luyts, P. (1992), “The accuracy of draft surveys”, Bulk Solids Handling, 12(2), pp. 266

6

Merks, J.W. (1985). Sampling and weighing of bulk solids, Trans Tech Publications, D-3392 Clausthal-Zellerfeld,

7

Merks, J.W. (1992), “Precision and bias for draft surveys and belt scales”, Bulk Solids Handling, 12(4), pp. 641-648

8

U.N. ECE (1992), “Code of Uniform Standards and Procedures for the performance of Draught Surveys of Coal Cargoes” (although this applies to coal the principles can be applied to many bulk commodities)

9

Australian Institute of Marine Surveyors, “Principles and Code of Practice for Draft Surveys (undated)

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B.4

MASS IN STORAGE AREAS

Guidelines: Section 3.4.7

B.5

1

Ooms, M. (1981), “Determination of Contents in Storage Bins and Silos”, Bulk Solids Handling, 1(4), pp. 651-665

2

Potts, A. (2001),“High Tech Surveying and Measuring,” Mining Magazine, December2001, pp.270-275

3

Cahill, P., Wright, H., Strutt, S., Wragg, D., (2000), Application of On-Line Laser Scanning Technology to Improve Unit Bunker Management in CoalFired Power Stations”, Bulk Solids Handling, 20(3) pp. 289-294

4

Lightbody, G., (1983), “How British Steel Manage Stockpiles”, Iron and Steel InternationaI, October 1983, pp178-182

5

McBride, W., Chambers, A.J., (1999), “Bulk Density Measurement Using a Shear Box,” Bulk Solids Handling, 19(4) pp. 501-505

SAMPLING

Code and Guidelines: Section 4 1

Gy, P. (1979), Sampling Of Particulate Materials – Theory and Practice. Elsevier.

2

Gy, P. (1982), Sampling Of Particulate Materials – Theory and Practice. 2nd Edition. Elsevier.

3

Merks, J.W. (1985), Sampling and weighing of bulk solids, Trans Tech Publications.

4

Smith, R. and James, G. V. (1981), “The Sampling of Bulk Materials”, The Royal Society of Chemistry.

5

Holmes R.J. (1992), “Sampling Processes in the Minerals Industry” Sampling of Mineral Process Streams, November 1992.

6

Holmes R.J. (2004), “Correct Sampling and Measurement – The Foundation of Accurate Metallurgical ing”, Chemometrics and Intelligent laboratory Systems, pp 71–83.

7

Bartlett, H.E. (2001). “Avoiding Metal ing Problems Through the Application of Statistics and Sample Design” Challenges in Metal ing and Information Management August 2001.

8

Bartlett, H. E. (2002). “Design of primary samplers for slurries in concentrators and statistical methods for measuring components of variance in sampling”, The Journal of The South African institute of Mining and Metallurgy, November/December 2002.

9

Bartlett, H. E. and Viljoen, R. (2002). “Variance relationships between the masses, grades and particle sizes for gold ores from the Witwatersrand”, ibid.

10 Francois-Bongarcon, D. and Gy, P. (2002). “Critical aspects of sampling in mills and plants: a guide to understanding sampling audits”, ibid. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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11 Francois-Bongarcon, D. and Gy, P. (2002). “The most common error in applying ‘Gy’s Formula’ in the theory of mineral sampling, and the history of the liberation factor”, ibid. 12 Hunt, C. A. (2002). “Comment on D. Francois-Bongarcon and P. Gy ‘Critical aspects of sampling in mills and plants: a guide to understanding sampling audits’ and H. E. Bartlett ‘Design of primary samplers for slurries in concentrators and statistical methods for measuring components of variance in sampling’”, ibid. 13 Pitard, F. F. (2002). “Practical and Theoretical Difficulties When Sampling Gold” Mineral Processing Plant Design, Practice and Control – Volume 1, Edited by L. Mular et al, Society of Mining, Metallurgy and Exploration. 14 Pitard, F. F. (1993). Pierre Gy’s Sampling Theory and Sampling Practice, 2nd Edition, CRC Press. 15 Merks, J. W. (2002). “Sampling in Mineral Processing”, ibid. 16 Lyman, G.J. (1993). Determination of bias in sampling systems, Minerals Engineering, 6 99–105. 17 Pitard, F. F. (2005). "Sampling Correctness – A Comprehensive Guideline," Proceedings of the Second World Conference on Sampling and Blending, Brisbane, May 2005. 18 Bartlett, H.E. and Hawkins, D.M., Chapter 13, 'Process Evaluation' in "The Extractive Metallurgy of Gold in South Africa" Vol 2, (1987) ed Stanley GG

B.6

ANALYSIS

Code and Guidelines: Section 5.3 1

Barwick V. (2003). “Preparation of Calibration Curves. A Guide to Best Practice”. LGC/VAM/2003/032

2

Hawkins DM and Olwell DH (1998). “Cumulative Sum Control Charts and Charting for Quality Improvement”, Springer.

3

J. C. Sherlock et al., “Analysis – Accuracy and Precision?”, Chem. Br., 1985, 21, 1019.

4

B. Griepink, “Certified Reference Materials (CRM’S) for the Quality of Measurement”, Fresenius’ J. Anal. Chem., 1990, 337, 812.

5

M. Sargent, “Development and Application of a Protocol for Quality Assurance of Trace Analysis”, Anal. Proc., 1995, 32, 71.

6

“Guidelines for Achieving Quality in Trace Analysis”, ed. M. Sargent and G. A. MacKay, The Royal Society of Chemistry, UK, 1995. ISBN 0-85404402-7.

7

F. M. Garfield, “Quality Assurance Principles for Analytical Laboratories”, Association of Official Analytical Chemists, 2nd edn, Arlington, VA, 1991.

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8

J. K. Taylor, “Quality Assurance of Chemical Measurements”, Lewis Publishers, Chelsea, MI, 1987.

9

“Quality Assurance for Analytical Laboratories” ed. M. Parkany, Special Publication no. 130, The Royal Society of Chemistry, UK, 1993, ISBN 085186-705-7.

10 G. V. Roberts, “Quality Assurance in Research and Development”, Marcel Dekker, New York, 1983. 11 J. K. Taylor and N. M. Trahey, “Handbook for SRM s”, NIST Special Publications 260-100, National Institute of Standards and Technology, Washington, DC, 1989. 12 “Uses of Certified Reference Materials’, ISO/IEC Guide 33, Geneva, 1989. 13 Analytical Methods Committee, “Internal Quality Control of Analytical Data”, Analyst (London), 1995, 120, 29. 14 E. Prichard, “Quality in the Analytical Chemistry Laboratory”, J. Wiley, Chichester, UK, 1995, ISBN 0471-95541-8 (5). 15 M. Thompson, “Variation of Precision and Concentration in an Analytical System”, Analyst (London), 1988, 113, 1579. 16 M. Thompson and R. Wood, “Harmonised Guidelines for Internal Quality Control in Analytical Chemistry Laboratories”, Pure Appl. Chem., 1995, 67, 649. 17 A. F. Bissell, “CUSUM techniques for Quality Control”, Appl. Stat. (UK), 1969, 18, 1. 18 J. M. Lucas and R. B. Crozier, “Fast Initial Response for CUSUM Quality Control Schemes: Give your CUSUM a Head Start”, Technometrics, 1982, 24, 199. 19 BS EN ISO 9001:1994, “Quality Systems – Model for Quality Assurance in Design/Development, Production, Installation and Servicing”, BSI, Chiswick, UK, 1994. 20 “General Requirements for the Competence of Calibrating and Testing Laboratories”, ISO Guide 25, Geneva, 3rd edn., 1990. 21 “The OECD Principles of Good Laboratory Practice”, Environment Monograph no. 45, OECD, Paris, 1992. 22 “Official and Standardized Methods of Analysis”, ed. C. A. Watson, The Royal Society of Chemistry, Cambridge, UK, 3rd edn, 1994, ISBN 085186-441-4. 23 J. K. Taylor, “Validation of Analytical Methods”, Anal. Chem., 1983, 55, 600A.

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B.7

METAL ING

Code and Guidelines: Section 7 1

R. Hantikainen and J. Alaluf, “Integrated Plant-wide Data Management in Mineral Processing Industries”.

2

R. Cargill, N. Freeman and R. Gilbertson, “Metal Balance of a large integrated operation using a Standard Industrial Software Package”, Olympic Dam, WMC, Australia.

3

F. Flament, M. C. Mound, C. Colbert and L. Fradette, (2004) “Practical integrated metallurgical ing solutions for ing production decisions”, Minerals and Metallurgical Processing, 21(1), pp 1-8

4

A. Smit, “Development of an Integrated Asset Management System for a Metallurgical Plant – Driefontein Gold Mine, Republic of South Africa”, Value Tracking Symposium, Brisbane 7th – 8th October, 2002.

5

R. D. Morrison and A. Grimes, “Turning Process Data into Process Information”, Challenges in Metallurgical ing and Information Management, SAIMM Cape Town, 1st August 2001.

6

J. M. Richardson, “Reconciliation of Mine Site, Mineral Processing Plant, Concentrate and Tailings Inventories,” Chapter 8

7

R. D. Morrison, “Material Balance Techniques,” Chapter 20

8

R. D. Morrison and M. E. Dunglison, “Improving DMS Operations Through Corporate Data Reconciliation,” Dense Media 2005, Paarl, South Africa, March 2005

9

J. A. Jones, R. D. Delster II, C. W. Hill, R. Barker and P. B. Crummie, “Process Control at Doe Run’s Fletcher Concentrator,” Mining Engineering, Dec 1991

10 R. D. Morrison, Y. Gu, W. McCallum, “Metal Balancing from Concentrator to Multiple Ore Sources,” Value Tracking Symposium, Brisbane, 7-8 October, 2002 11 D. O’Sullivan and L. Barnes, ”The Development of KPI and Metallurgical ing Systems at Nabalco,” Value Tracking Symposium, Brisbane, 7-8 October, 2002 12 A.F.Taggart, 1945. Handbook of Mineral Dressing. 13 Vendors of Metal ing Software: 13.1 Algosys Inc, Quebec, Canada. 13.2 CitectIIM, Citect, South Africa. 13.3 Knowledge Manager – Industrial IT for Metal ing, ABB. 13.4 JKMet, JK Institute, Australia. 13.5 Assaynet – Custom systems, Assaynet. 13.6 Uniformance PHD – Metal ing Solution using a data historian as the basis, Honeywell. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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14 Mine Metallurgical Managers Association of South Africa Circular 1 95 Gold Lock-up in Plants 15 M.L. Fitzgerald, “Metallurgical ing and Control,” Trans IMM, Vol.69, 1959-60, pages 19-42 16 P Guerney, M Dunglison and P M Cameron. "Leveraging Accuracy and Precision – Multi-Phase Mass Balancing and Reconciliation as a Tool for Quality Data Management", Proceedings of the Second World Conference on Sampling and Blending, Brisbane, May 2005.

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APPENDIX C – STANDARDS C.1

MASS MEASUREMENT

C.1.1 STATIC MASS MEASUREMENT 1

SABS 1649:1995: Non-automatic self-indicating and semi-self-indicating weighing instruments. (Annexure B gives the requirements for verification and shows maximum tolerances that are permissible for new installations as well as instruments in service)

2

SABS 1697:1998: Verification Standards for Verification of MassMeasuring Instruments, including commercial standard mass pieces

3

SABS 0259:1990 (ISO/IEC Guide 25:1990): General requirements for the competence of Calibration and Testing laboratories and ARP 023 Recommended practice for the Application of SABS 0259 to the Verification of mass measuring instruments under the trade Metrology Act, 1973

4

NIST Handbook 44: Specifications, Tolerances and other Technical Requirements for Weighing and Measuring Devices Section 1 General and Section 2.20 Scales

5

OIML R51-1 and 2: Automatic Catch Weighing Instruments Part 1 & 2

6

OIML R60 2000: Metrological Regulation for Load Cells

7

OIML R76-1and 2 1992: Non-Automatic Weighing Instruments Part 1 & 2

8

OIML R107-1and 2: Discontinuous totalising Weighing Instruments (totalising Hopper Weighers) Part 1and 2

9

ISO 10251: Procedure for the determination of moisture.

10 ISO 12743: Procedure for the taking of samples for the determination of moisture 11 ISO 12745: Copper, Lead and Zinc Ores and Concentrates--Precision and bias of mass measurement techniques 12 ISO 13543: Copper, Lead and Zinc Sulphide concentrates--Determination of mass of contained metal in a lot 13 ISO 17025:1999 General Requirements for the Competence of Testing and Calibration Laboratories 14 VIM 1993: International Vocabulary of Basic and General in Metrology 15 VML 1978: Vocabulary of Legal Metrology 16 BS 8422 2003: Force measurement. Strain gauge load cell systems. Calibration Method Although standards 11 and 12 apply to base metal concentrates, the basic principles can be applied to most materials and the calibration methodology can be applied to any mass measurement.

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C.1.2 IN-MOTION WEIGHING 1

NIST Handbook 44 Section 2.20

2

OIML R106-1and 2 1993/1997: Automatic Rail bridges Part 1 and 2

3

OIML R134-1and2 2003/4: Automatic in-motion Road weighing Part 1 and 2

C.1.3 BELT WEIGHERS 1

NIST Handbook 44 Section 2.22

2

OIML R50-1 and 2 1997: Continuous totalising weighing instruments (Belt Weighers)

3

ISA-RP74.01-1984: Application and Installation of Continuous-Belt Weighbridge Scales

4

JISB 7606: Belt Weighers

5

South African Trade Metrology Act And Regulations (77/93) Section 44

C.1.4 MASS FLOW IN CONDUITS 1

ISO Guide: Guide to the expression of Uncertainty in measurement First Edition 1995 International Organisation for Standardisation

2

ISO 2186: Connections for pressure signal transmissions

3

ISO 2714: Volumetric measurements by displacement meter systems other than dispensing pumps

4

ISO 2715: Volumetric measurement by turbine meter systems

5

ISO 4006:1991: Measurement of fluid flow in closed conduits - Vocabulary and Symbols

6

ISO 4064-2: Meters for cold potable water

7

ISO 4185:1993: Measurement of liquid flow in closed conduits – Weighing method

8

ISO 5167-1:2003:Measurement of fluid flow by means of pressure differential devices inserted in circular cross-section conduits running full – Part 1:General principles and requirements

9

ISO 5167-2:2003: Measurement of fluid flow by means of pressure differential devices inserted in circular cross-section conduits running full – Part 2: Orifice Plates

10 ISO 5167-3:2003: Measurement of fluid flow by means of pressure differential devices inserted in circular cross-section conduits running full – Part3: Nozzles and Venturi Nozzles 11 ISO 5167-4:2003: Measurement of fluid flow by means of pressure differential devices inserted in circular cross-section running full - Part4: Venturi tubes AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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12 ISO/TR 5168:1998: Measurement of fluid flow – Evaluation of uncertainty 13 ISO 5725:1994, parts 1-6: Accuracy (trueness and precision) of measurement methods and results 14 ISO 6817:1992: Measurement of conductive liquid flow in closed conduits – Method using electromagnetic flow meters 15 ISO/TR 7066-1:1997: Assessment of uncertainty in calibration and use of flow measurement devices - Part 1: Linear calibration devices 16 ISO/TR 7066-2:1997: Assessment of uncertainty in calibration and use of flow measurement devices - Part 2: Non-linear calibration relationships 17 ISO 7278: Dynamic measurement. Proving systems for volumetric meters; Part 2: Pipe provers 18 ISO 8316:1987: Measurement of fluid flow in closed conduits – method by the collection of the liquid in a volumetric tank 19 ISO 9104:1991: Measurement of fluid flow in closed conduits- Methods of evaluating the performance of electromagnetic flow-meters for liquids 20 ISO 9300: Measurement of gas flow by means of critical flow venturi nozzles 21 ISO 9368: Measurement of liquid flow in closed conduits by the weighing method. Procedures for checking installations. Part 1: Static weighing systems 22 ISO 9951: Measurement of gas flow in closed conduits – turbine meters 23 ISO 10790: 1999/2003: Measurement of fluid flow in closed conduitsGuidance to the selection, installation and use of Coriolis meters (mass flow, density and volume flow measurements) 24 ISO 11605: Calibration of variable area flow meters 25 ISO 11631:1998: Measurement of fluid flow – Methods of specifying flow meter performance 26 ISO/TR 12764: Flow rate measurement of fluid flow means of vortex shedding flow meters 27 ISO /TR 12765: Methods using transit time ultrasonic flow meters 28 ISO 12767: Guidelines to the effect of departure from the specifications and operating conditions given in ISO 5167 29 ISO 13359: Flanged-type electromagnetic flow meters – overall lengths 30 ASME MFC-1M: Glossary of used in measurement of fluid flow in pipes 31 ASME MFC- 2M: Measurement uncertainty of fluid flow in closed conduits 32 ASME MFC-3M: Metering flow by means of orifices, nozzles and venturis. 33 ASME MFC-6M: Metering flow in closed conduits by means of vortex-type meters

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34 ASME MFC-10M: Method of establishing installation effects on flow meters 35 ASME MFC-11M-2003: Measurement of fluid flow by means of Coriolis Mass Flow meters 36 ASME MFC-16M-1995: Measurement of fluid flow in closed conduits by means of Electromagnetic flow-meters 37 ASME MFC-18M: Metering flow by means of variable area meters 38 ASME PTC-19.5: Application of fluid meters 39 AS 2360.1.2 1993 (BS 1042: Section 1.2:1993): Measurement of Fluid Flow in Closed Conduits. Part 1.2: Pressure differential methods— Measurement using orifice plates or nozzles- Conduits with diameters less than 50mm. 40 AS 2360.1.3 1993 (BS 1042: Section 1.4 1992): Measurement of Fluid flow in Closed Conduits. Part 1.3: Pressure differential methods— Measurement using orifice plates, nozzles or Venturi tubes – Guide to the use of methods specified in Parts 1.1 and 1.2. 41 OIML R 10: 1993: Direct Mass Flow Measuring Systems for Quantities of Liquids. 42 OIML P17: Guide to the expression of uncertainty in measurement (GUM) 43 OIML D25: Vortex meters (used in measuring systems for fluids 44 OIML R105C: Direct mass flow measuring systems for quantities of liquids 45 BS EN 7405: Guide to the selection and application of flow meters for the measurement of fluid flow in closed conduits

C.1.5 MASS FLOW IN OPEN CHANNELS 1

ISO 748: Measurement of liquid flow in open channels – Velocity-area methods

2

ISO 1438-1:1980: Water flow measurement in open channels using weirs and venturi flumes – Part1: Thin plate weirs

3

ISO 2537: Liquid flow measurements in open channels – Rotating element current meters

4

ISO 4377: Hydrometric determinations – Flow measurement in open channels using structures – Flat-V weirs

5

ISO/TR 8363:1997: Measurement of liquid flow in open channels- General guidelines for selection of method

6

ISO 9555: Measurement of liquid flow in open channels – tracer dilution methods for measurement of steady flow

7

ISO 9826: Measurement of flow in open channels; Parshall and SANIIRI flumes

8

ISO/TS 15768: Measurement of liquid velocity in open channels – Design, selection and use of electromagnetic current meters.

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C.2

SAMPLING

C.2.1 GENERAL 1

JIS M 8100:1992 Particulate Materials – General Rules for Methods of Sampling

2

JIS M 8702:2002: Iron Ores – Sampling and Sample Preparation Procedures

3

JIS Z 8816:2001 Particulate Materials – General Requirements for Methods of Sampling

4

AS 2884.1–1997 Heavy mineral sand concentrates – Sampling. Part 1: Moving Streams

5

AS 4433.1–1997 Guide to the Sampling of Particulate Materials. Part 1: Sampling Procedures

6

AS 4433.2–1997 Guide to the Sampling of Particulate Materials. Part 2: Preparation of Samples

7

AS 4433.3–2002 Guide to the Sampling of Particulate Materials. Part 3: Estimating Sampling Precision

8

AS 4433.4–2001 Guide to the Sampling of Particulate Materials. Part 4: Checking for Bias

9

AS 4433.5–2001 Guide to the Sampling of Particulate Materials. Part 5: Sampling of Slurries

10 AS 4433.6–2000 Guide to the Sampling of Particulate Materials. Part 6: Inspection of Mechanical Sampling Systems 11 ISO 11648–2:2001 Statistical Aspects of Sampling from Bulk Materials – Part 2 Sampling of Particulate Materials

C.2.2 COAL 1

ISO 1953:1994 Hard Coal - Size analysis by sieving

2

ISO 1988:1975 Hard Coal - Sampling

3

ISO 13909 – 1:2001 Hard Coal and Coke – Mechanical Sampling Part 1 General Introduction

4

ISO 13909 – 2:2001 Hard Coal and Coke – Mechanical Sampling Part 2 Coal – Sampling from moving streams

5

ISO 13909 – 3 Hard Coal and Coke – Mechanical Sampling Part 3 Coal Sampling from stationary lots

6

ISO 13909 – 4 Hard Coal and Coke – Mechanical Sampling Part 4 Coal Preparation of test samples

7

ISO 13909-7:2001 Hard Coal and Coke - Mechanical Sampling Part 7 Methods for Determining the Precision of Sampling, Sample Preparation and Testing

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8

ISO 20905:2004 Coal Preparation – Determination of dust/moisture relationship for coal

9

ASTM D2013: Standard Practice of Preparing Coal Samples for Analysis

10 ASTM D3302a: Standard Test Method for Total Moisture in Coal

C.2.3 IRON ORES AND FERROALLOYS 1

ASTM E877 – 03: Standard Practice for the Sampling and Sample Preparation of Iron Ores and Related Materials.

2

ISO 3082:2000 Iron Ores – Sampling and Sample Preparation Procedures

3

ISO 3083:1973 Iron Ores – Preparation

4

ISO 3085:2002: Iron Ores – Experimental Methods for Checking the Precision of Sampling, Sample Preparation and Measurement

5

ISO 3086:1998: Iron Ores – Experimental Methods for Checking the Bias of Sampling

6

ISO 3087:1998 Iron Ores – Determination of the Moisture Content of a Lot

7

ISO 3713:1987 Ferroalloys – Sampling and preparation of samples – General Rules

8

ISO 3852:1988 Iron Ores – Determination of Bulk Density

9

ISO 4551:1987 Ferroalloys—Sampling and Sieve Analysis

10 ISO 4552-1 Ferroalloys: Sampling and Sample Preparation 11 ISO 4552-2 Ferroalloys: Sampling and Sample Preparation for Chemical analysis 12 ISO 7087:1984 Ferroalloys – Experimental methods for the evaluation of the quality variation and methods for checking the precision of sampling 13 ISO 7347:1987 Ferroalloys – Experimental methods for checking the bias of sampling and sample preparation 14 ISO 7373:1987 Ferroalloys Experimental methods for checking the precision of sample division 15 ISO 7764:1985 Iron Ores – Preparation of Pre-dried Test Samples for Chemical Analysis 16 ISO 8049:1988 Ferronickel shot – Sampling for analysis 17 ISO 8050:1988 Ferronickel ingots or pieces – Sampling for analysis 18 ISO 10836:1994: Iron Ores – Method of Sampling and Sample Preparation for Physical Testing 19 ISO 14284:1996 Steel and Iron – Sampling and preparation of samples for the determination of chemical composition

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C.2.4 NON-FERROUS MATERIALS 1

JIS M 8101:1998: Methods of Sampling, Preparation and Determination of Moisture Content in Non-ferrous Metal Bearing Ores

2

JIS M 8108:1992: Chromium, Manganese and Ferruginous Manganese Ores – Sampling and Determination of Moisture Content and Size Distribution

3

ASTM E2293-03: Standard Practice for Drying Metal Bearing Ore

4

ISO 12742:2000: Copper, Lead and Zinc Sulphide Concentrates – Sampling Procedures for the Determination of Metal and Moisture Content

5

ISO 12743: Copper, Lead and Zinc Sulphide Concentrates – Sampling Procedures for the Determination of Metal and Moisture Content

6

ISO 12744:1997: Copper, Lead and Zinc Sulphide Concentrates – Experimental Methods for Checking the Precision of Sampling

7

ISO 12745:1996: Copper, Lead and Zinc Sulphide Concentrates – Precision and Bias of Mass Measurement Techniques

8

ISO 13292:1997: Copper, Lead and Zinc Sulphide Concentrates – Experimental Methods for Checking the Bias of Sampling

9

ISO 13543:1996 Copper, Lead and Zinc Sulphide Concentrates – Determination of Mass of Contained Metal in a Lot

10 ISO 20081:2005 Zinc and zinc alloys – Method of sampling – specifications 11 ISO 1811 – 1:1988 Copper and copper alloys – Selection and preparation of samples for chemical analysis – Part 1 Sampling of cast unwrought products 12 ISO 1811 – 2:1998 Copper and copper alloys – Selection and preparation of samples for chemical analysis – Part 2 Sampling of wrought products and castings 13 ISO 4296 – 1:1984: Manganese Ores – Sampling Part 1. Increment Sampling 14 ISO 4296 – 2:1983: Manganese Ores – Sampling Part 2. Preparation of samples 15 ISO 4299:1989 Manganese Ores – Determination of Moisture Content 16 ISO 6140:1991: Aluminium Ores – Preparation of Samples 17 ISO 6153:1989: Chromium Ores – Increment Sampling 18 ISO 6154:1989: Chromium Ores – Preparation of Samples. 19 ISO 6208:1992 Nickel and nickel alloy Plate, sheet and strip 20 ISO 7156:1991 Refined Nickel: Sampling

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21 ISO 8541:1986: Manganese and Chromium Ores – Experimental Methods for Checking Bias of Sampling and Sample Preparation 22 ISO 8542:1986: Manganese and Chromium Ores – Experimental Methods for Evaluation of Quality Variation and Methods for Checking Precision of Sampling 23 ISO 8685:1992: Aluminium Ores – Sampling Procedures 24 ISO 8868 Fluorspar: Sampling and Sample Preparation 25 ISO 10226:1991 Aluminium Ores – Experimental Methods for Checking Bias of Sampling 26 ISO 10227:1995: Aluminium Ores – Experimental Methods for Checking Precision of Sampling

C.3

ANALYSIS

C.3.1 ISO STANDARDS 1

ISO 17025:1999 General requirements for the competence of testing and calibration laboratories

2

ISO Guide 30:1992 and definitions used in connection with reference materials

3

ISO Guide 31:2000 Contents of certificates of reference materials

4

ISO Guide 32:1997 Calibration of chemical analysis and use of certified reference materials

5

ISO Guide 33:2000 Uses of certified reference materials

6

ISO Guide 34:2000 General requirements for the competence of reference material; producers

7

ISO Guide 35:1989 Certification of reference materials-General and statistical principles

8

ISO/REMCO Document N330 List of producers of certified reference materials. Information by Task Group 3 “Promotion”

9

ISO 9000:2000 Quality Management Systems – Fundamentals and vocabulary

10 ISO Guide to the Expression of Uncertainty in Measurement 11 ISO/CASCO 193 (Rev. 2) – ISO/CASCO discussion paper providing upto-date references on terminology between full updates of ISO Guide 2 12 ISO Guide 2:1996 Standardisation and related activities – General vocabulary 13 ISO 8402:1994 Quality – Vocabulary 14 ISO 10011-1:1990 Guidelines for auditing quality systems. Part 1: Auditing AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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15 ISO 10011-2:1991 Guidelines for auditing quality systems. Part 2: Qualification criteria for quality system auditors 16 ISO 10011-3:1991 Guidelines for auditing quality systems. Part 3: Management of audit programmes 17 ISO 3534-1:1993 Statistics – Vocabulary and symbol – Part 1: Probability and general statistical 18 ISO 3534-2:1993 Statistics – Vocabulary and symbol – Part 2: Statistical quality control 19 ISO 3524-3:1985 Statistics – Vocabulary and symbol – Part 3: Design of experiments 20 ISO 7870:1993 Control charts – General Guide and Introduction 21 ISO 7873:1993 Control charts for arithmetic average with warning limits 22 ISO 7966:1993 Acceptance control charts 23 ISO 8258:1991 Shewhart control charts 24 ISO VIM International vocabulary of basic and general in metrology, 2nd ed., 1993 25 ISO 11095:1993 Linear calibration using reference materials

C.3.2 ILAC PUBLICATIONS 1

ILAC-I1: 1994 Legal Liability in Testing

2

ILAC-I2: 1994 Testing, Quality Assurance, Certification and Accreditation

3

ILAC-I3: 1996 The Role of Testing and Laboratory Accreditation in International Trade

4

ILAC-I4: 1996 Guidance Documents for the Preparation of Laboratory Quality Manuals

5

ILAC-G2: 1994 Traceability of Measurement

6

ILAC-G3: 1994 Guidelines for Training Courses for Assessors

7

ILAC-G4: 1994 Guidelines on Scopes of Accreditation

8

ILAC-G8: 1996 Guidelines on Assessment and Reporting of Compliance with Specification

9

ILAC-G9: 1996 Guidelines for the Selection and Use of Certified Reference Materials

10 ILAC-G10: 1996 Harmonised Procedures Reassessment of Accredited Laboratories

for

Surveillance

&

11 ILAC-G11: 1998 Guidelines on Assessor Qualification and Competence 12 ILAC-G15: 2001 Guidance for Accreditation to ISO 17025

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13 ILAC-G17: 2002 Guidance for Introducing the Concept of Uncertainty of Measurement in Testing in Association with the Application of the Standard ISO 17025

C.3.3 CITAC/EURACHEM PUBLICATIONS 1

CITAC/EURACHEM Guide CG1: 2002 Guide to Quality in Analytical Chemistry

2

CITAC/EURACHEM Guide CG4: 2000 Quantifying Uncertainty in Analytical Chemistry

3

CITAC/EURACHEM Guide 2003 Traceability in Chemical Measurement

4

CITAC/EURACHEM Quality Assurance for Research and Development and Non-Routine Analysis

5

CITAC/EURACHEM Guide The Fitness for Purpose of Analytical Methods

C.3.4 UKAS PUBLICATIONS 1

UKAS Publication ref:LAB 7 Group Laboratories Accreditation

2

UKAS Publication ref:LAB 12 The Expression of Uncertainty in Testing

3

UKAS Publication ref:LAB 13 Guidance on the Application of ISO 17025 Dealing with the Expressions of Opinions and Interpretation

4

UKAS Publication ref:LAB 14 Calibration of Weighing Machines

5

UKAS Publication ref:LAB 15 Traceability: Volumetric Apparatus

6

UKAS Publication ref:LAB 27 Accreditation for Chemical Laboratories

7

UKAS Publication ref:LAB 39 Guidance on the Implementation and Management of Flexible Scopes of Accreditation within Laboratories

C.3.5 ASTM PUBLICATIONS 1

ASTM E1479 – 99: Standard Practice for Describing and Specifying Inductively-Coupled Plasma Atomic Emission Spectrometers.

2

ASTM E1577 – 95 (2000): Standard Guide for Laboratory Information Management Systems (LIMS).

3

ASTM E1587 – 94 (1998) Standard Methods for Chemical Analysis of Refined Nickel.

4

ASTM E1601 – 98 (2003) e1: Standard Practice for Conducting an Interlaboratory Study to Evaluate the Performance of an Analytical Method.

5

ASTM E1691 – 95 (2001): Standard Guide for X-ray Emission Spectrometric Analysis.

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6

ASTM E1691 – 95 (2001): Standard Guide for Evaluation and Assessment of Analytical Chemical Laboratories.

7

ASTM E1724 – 95 (2001): Standard Guide for Testing and Certification of Metal and Metal Related Reference Materials.

8

ASTM E1763 – 98 (2003): Standard Guide for Interpretation and Use of Results from Inter-laboratory Testing of Chemical Analysis Methods.

9

ASTM E1950 – 98 (2003): Standard Practice for Reporting Results from Methods of Chemical Analysis.

10 ASTM E2028 – 99 Standard Practice for Receiving, Testing and Reporting Results of Investigations of Metal, Ore or Metal Related Samples that Are or May Be Involved in Litigation. 11 ASTM E2053 – 00: Standard Guide for Planning, Carrying Out and Reporting Traceable Chemical Analysis of Metals, Ores and Related Materials. 12 ASTM E2066 – 00: Standard Guide for Validation of Laboratory Information Management Systems. 13 ASTM E50- 00: Standard Practices for Apparatus, Reagents and Safety Considerations for Chemical Analysis of Metals, Ores and Related Materials. 14 ASTM E882 – 87 (2003): Standard Guide for ability and Quality Control in the Chemical Analysis Laboratory. 15 ASTM D6091 Standard Practice for 99%/95% Inter-laboratory Detection Estimate (IDE) for Analytical Methods with Negligible Calibration Error

C.3.6 NATA PUBLICATIONS 1

NATA December 2002 Laboratory Assessment Worksheet

2

NATA May 2003 17025 Checklist with Interpretive Comments

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APPENDIX D – CHECK-LISTS D.1 MASS MEASUREMENT D.1.1 GENERAL CRITERIA

EXPLANATION

Certification (Assizing)

The procedure, which is a legal requirement, whereby a mass or volume measuring instrument is approved as usable for Custody Transfer and trade purposes and, in of this Code, for Primary Metal ing, which must be performed at laid down intervals by the relevant government authority or their authorised agents. Procedures are laid down in National Metrology Acts.

Calibration

The procedure (as per National Metrology Acts, OIML Recommendations, the NIST Handbook, ISO or other applicable standards) for checking a mass or flow measuring device, which must be performed at periodic intervals and at a sufficient frequency, to measure the error in the result reported by the device against a standard known mass or volume. This is in order to ensure that the reported mass or flow measurements remain within the required precision stipulated for Primary Metal ing or Custody Transfer.

Exception Report

Required for all mass/flow measurements in cases where the Code requirements cannot be adhered to. Must be fully motivated with reasons, a plan for improvement and approved by Senior Management.

Non-Conformance Report

Required for all mass/flow measurements in cases where an internal or independent Metal ing Review has found that the Code requirements have not been met and/or where there is no Exception Report approved. It must contain the reasons why the Code requirements have not been met and recommendations for improvements and it must be signed off by the Internal audit team leader or the Competent Person (if as a result of an independent Metal ing Review).

Records

Records of all certification and calibration test results must be kept for five years. These records must show all the results and measured errors and document any alterations and adjustments made to the instrument and installation.

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D.1.2 STATIC AND IN-MOTION WEIGHING CRITERIA

EXPLANATION

Test Weights

Static weights, and in the case of In-Motion weighing, weighed railway wagons, used for calibration must have been weighed on a test facility and certified to be accurate to the required tolerance. In the case of Weigh Bins/Hoppers and Gantry Scales, facilities for loading and unloading the test weights on to the framework are required.

Structure for Bins

The structure ing Weigh bins/Hoppers must be robust enough to minimise deflection and to ensure that the load is transferred evenly onto the load sensors.

In-Motion Weighing

Truck speed must be limited and controlled to ≤8 km/hr, rail trucks to be weighed empty (tared) on the weigher on every occasion before/after filling or emptying. The rails must be straight and firmly held in concrete in the vicinity of the weigher. Track must be well maintained to limit impact, shocks and vibration

Preferred Method

Static weighing is the preferred method for all primary ing if feasible, as it is the most precise and it has greater sensitivity

D.1.3 CONVEYOR BELT WEIGHING CRITERIA

EXPLANATION

Type of Weigher

Whether the weigher is nuclear or electromechanical (approachretreat, pivoting or fully suspended), fitted with counterweight or not, type and number of load sensors, the number of weigh idlers, the method of belt speed pickup and sensing and the type of micro processor. Nuclear weighers are not suitable for Primary Metal ing or Custody Transfer.

Calibration

Procedures such as bulk (material) tests, test chains, belt cuts, certified static weights or electronic procedures used to check the precision of conveyor belt weighers. For Primary Metal ing, bulk tests as per approved standards are mandatory and thus facilities for accurate check pre- or post-weighing of the bulk material ing over the weigher are required. Calibration to include zero and span checks over 20-100% of the operating range, for ten minutes or three belt revolutions whichever is the greater, repeated three times to check repeatability as specified in OIML Recommendations, the NIST Handbook or other applicable standards. No ISO standard exists for conveyor belt weighers.

Conveyor Belt

Endless, with smooth spliced ts and as flexible as possible, sized to give a minimum loading of 20% and an operating range

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EXPLANATION of 60-80% of capacity.

Class of Weigher

The three classes as per OIML 50 with specified maximum errors on verification and in service. For Primary Metal ing belt weighers must meet the requirements of OIML 50 Class 0,5 (a maximum permissible error in service of ± 0,5%).

Conveyor System

To facilitate obtaining the required accuracy, the conveyor system should meet as many as possible of the Best Practice requirements, in the Code and Standards, for design, installation, operation and maintenance, with respect to the conveyor layout and structure, position of the weigher in relation to factors which may affect it (such as curves in the belt, the feed system, skirting, training idlers), feed system, idlers, gravity/automatic take-up, and protection from the environment. In general these requirements are in order to ensure as low a tension in the belt as possible, a steady feed with no overloading of the belt, rigid construction of the conveyor and weigh suspension with little deflection, careful alignment of belt and weigh idlers, protection from wind and weather and cleanliness of the weigh suspension.

Belt Speed Pickup

By cylinder or wheel directly in with belt surface, near the weigh span, installed at 90 degrees to the direction of belt travel in such a manner as to minimise slip. To be kept clean at all times.

Integrating System

Computes, indicates, totals and records the mass flow of material over the belt and the instantaneous flow rate, from the mass on the belt and the belt speed.

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D.1.4 FLOW MEASUREMENT CRITERIA

EXPLANATION

Type of Flow meter

Careful selection and specification from many types to suit the specific application. For Metal ing the choice for slurries is limited to Electromagnetic flow meters, or in a few cases, Coriolis or Ultrasonic types. All except the Coriolis, measure volume flow and thus require relative density/specific gravity measurement (See E.1.6), which can introduce significant error. Manufacture, selection and installation must adhere to the applicable standards (ISO 6817, ISO 9104, ISO10790, ISO 13359, ASME MFC-11M2003, ASME MFC-16M-1995)

Specifications and Performance

The uncertainty of the instrument must be specified in of ISO 11631 with traceability back to the approved test meter/facility preferably according to Group A but at least Group B.

Certification

The initial calibration and certification to be in of ISO 4185 or ISO 8316, in a facility accredited in accordance with ISO 17025, and the uncertainty assessed in of ISO 7066 and 5168.

Calibration

For Primary ing the meters must be calibrated, on the material being measured, on a regular basis utilising the principles in ISO 4185 or 8316. This requires accurate volumetric/weighing tanks and an efficient diversion system to obtain flow rate comparisons. Relative density/specific gravity measurement is as important as the flow (See E.1.6.).

D.1.5 STOCKS AND INVENTORY CRITERIA

EXPLANATION

Stockpiles

Reduce to zero to establish absolute value and/or manage to measure mass of receipts and withdrawals accurately to calculate stock by difference. Use of parallel stockpiles to enable regular emptying. Volumetric surveys only to be used where the above is impossible on the understanding that the results will only be as good as sampling and bulk density determination and accuracy allow. No international standards exist.

Process Inventory “Bubble method” for high value materials and use of parallel streams or vessels, to facilitate emptying Audits

Internal and Independent audits to check and recommend corrections, if any, for approval by Competent Person.

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D.1.6 DENSITY AND MOISTURE MEASUREMENT CRITERIA

EXPLANATION

Moisture

Required in all cases as mass measurement instruments measure wet mass. Sampling for moisture must conform with this Code, ensuring that the assigned moisture is applicable to the material being weighed.

Density of Slurries

Density gauges must be calibrated on the actual material being measured and accurate relative densities of both solid and liquid phases are required.

Bulk Density

Variable even for the same material as it is affected by many factors such as particle size and compaction. Most reliable method is measurement of the mass of a known volume.

D.2 SAMPLING CRITERIA

EXPLANATION

Type of sampler

Whether the primary sampler is a cross-stream sampler from a conveyor belt or slurry stream, a hammer sampler or an auger sampler. If it is a cross-stream sampler, what are the design criteria of the system e.g. gap size, cutter speed, radial or parallel etc.?

Multi-stage

If it is a multi-stage sampling system: The number of stages, whether there is sample reduction between stages and the percentage cut at each stage, together with the total percentage cut. Experimentation undertaken to ensure sample division does not introduce excessive variance.

Sampling system

Sampling Interval

The basis of the time interval between increments.

Batch Size

The basis of the batch size i.e. is it on a time (shift) or tonnage (lot) basis.

Sub-sampling

Whether any further sample division is done on the plant and, if so, details of how this is done.

Drying conditions

If sample drying is carried out on the plant, the drying temperature and duration.

Sample labelling

Label type and sample identification details.

Quality checks

Whether the sample weight at each stage is related to the expected weight based on process flows, cutter gap, cutter speed and sampling interval.

Inspections

The frequency of inspections and level of employee.

Fault reporting and rectification

The procedure for reporting faults, their rectification and the g-off by management.

Exception Reports

Reports on non-compliance with the Code must contain the reasons for non-compliance, action plans to reach compliance

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CRITERIA

EXPLANATION and be signed-off by the Competent Person.

D.3 SAMPLE PREPARATION CRITERIA

EXPLANATION

Sub-sampling

The mass and top particle size of the sample received by the laboratory. Whether further sample division is necessary before drying and, if so, details of how this is done.

Drying conditions

Drying temperature and duration

Sample reduction

Type of equipment used e.g. jaw crushers, pulverisers, ring and puck mills etc. Action taken to guard against dust loss, contamination from the equipment e.g. iron, chromium and cobalt and cross-contamination.

Sample division

Type of equipment used e.g. spinning rifflers, turning tube dividers etc. Experimentation undertaken to ensure that excessive variance is not introduced by sampling division. Action taken to guard against dust loss and crosscontamination.

Sample mixing

Type of equipment used e.g. Y-blenders, cube blenders etc. and duration.

Sample storage

Details of container and storage conditions. Duration of storage and authorisation level for disposal.

Quality checks

Contamination from sample reduction equipment. Particle size analysis after each reduction stage. Splitting ratios from the sample division equipment.

Inspections

The frequency of inspections and the level of employee.

Fault reporting

The procedure for reporting faults, their rectification and the g-off by management.

and rectification Exception Reports

Reports on non-compliance with the Code must contain the reasons for non-compliance, action plans to reach compliance and be signed-off by the Competent Person.

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D.4. ANALYSIS CRITERIA

EXPLANATION

Accreditation

The nature, quality and appropriateness of the analytical procedure must be confirmed by accreditation to ISO 17025.

Twin-stream (Independent Parallel) analysis

Details of the twin-stream system, preferably different analysts on different days, and the measured reproducibility.

Reference Materials

Whether the RMs are in-house or CRMs. In particular how the accepted values were obtained and verified.

Instrument calibrations

The number of standards used, linearity of the calibration range, reproducibility of the calibration, frequency of recalibration. Use of internal standards. Frequency of preparation of the standards and their storage conditions. Source of the primary standards.

QC procedures

Use of QC samples, splitting limits between duplicates.

QA procedures

Round-robin exercises.

Umpire laboratory

Selection of the umpire laboratory, steps taken to maintain quality of analysis.

Maintenance

Maintenance contracts, maintenance records.

Fault reporting

The procedure for reporting faults, their rectification and the g-off by management.

and rectification Exception Reports

Reports on non-compliance with the Code must contain the reasons for non-compliance, action plans to reach compliance and be signed-off by the Competent Person.

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D.5. REPORTING CRITERIA

EXPLANATION

Metal Balance

Desired constituents over the reporting period, timeously to Plant Management and Financial ants

Key Performance Indicators (KPI’s)

Feed and product tonnages and grades; recoveries, abilities; reported against target figures; error levels of all reported outputs; historical comparisons to assist monitoring of long-term trends

Traceability

To all sources of inputs

Data sources

Manual entries to be minimised and automatic data collection from SCADA or LIMS systems maximised

Preparation

Adequately trained personnel, who understand source and meaning of the inputs

Adjustments or alterations

Only with written authorisation of responsible person and recorded for review purposes

Mass balance reconciliation

Only via an approved procedure to identify inputs subject to illegitimate (spurious) errors, to determine bias and to facilitate remedial action

Approval

Reviewed and signed off by Metal ing Committee, or equivalent, for subsequent approval by Audit Committee

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D.6. AUDITING CRITERIA

EXPLANATION

Compliance with Code

Suitably trained outside of the area they are examining must carry out audits. They must ensure that the requirements of the Code as listed in Check Lists E.1. to E.6. have been adhered to.

Audit Types

There are four types of audit: Inspections by operating staff – These must be carried out daily and should cover areas such as cleanliness, operational problems and calibration. Internal Audits – These must be carried out at least monthly by supervisory staff or suitably trained staff from other sections. External Audits – These must be carried out on a “surprise” basis at least twice a year by suitably trained and qualified staff, preferably including a financial auditor, who are independent of the facility being audited, but more frequently if conditions of bias or malpractice have been found. These audits must contain both horizontal audits, where the auditor will examine in detail single aspects of the quality system e.g. calibration, and vertical audits, where the auditor will select a sample and follow its progress from mass measurement to storage and/or disposal. The management of stockpiles, especially any reduction to zero or sudden change in process inventory, must be commented on. Stock-takes – These must be witnessed by an external audit team and preferably be based on the bubble method.

Audit Reports

Audit Reports should be based on pre-designed hard-copy checklists, which must be retained as a record for at least 5 years. Recommended examples may be found in the Australian Standard AS 4433.6 – 2000, for sampling equipment, and CITAC/Eurachem Guide CG1 – 2002, for laboratories.

Non-Conformance Reports

Non-Conformance Reports must be issued, with appropriate comments, for each instance of non-conformance. The appropriate operational management must comment on these reports before they are submitted to higher management.

Exception Reports

Exception Reports should be examined and any progress, or lack of, on the elimination of problems commented on.

Metal ing Reviews

The whole metal ing system must be reviewed on at least an annual basis by senior management to establish that it is still fulfilling its function. The review findings must be signedoff by the Audit/Risk Committee and endorsed by the Competent Person.

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APPENDIX E – MASS BALANCING TECHNIQUES E.1 SIMPLE EXAMPLE (R.D.Morrison, private communication) One objective of metal balancing calculations is to carry out calculations which can utilise all of the available data. The calculation process will both expose inconsistencies in the data and identify the likely source of the inconsistency. It will also provide a way to link data from many sources, to integrate the data in a structured manner and to provide a key source of tested, verified or certified information which can be used as a basis for sound decision-making. The use of redundant information will necessarily produce differences, raising the question as to how these differences should be treated. The following simple example illustrates the principles outlined in Section 7 using unsophisticated mathematical techniques. The example is drawn from Morrison, Gu and McCallum (2002). This example demonstrates how a mass balancing problem can be solved in two stages. Although it is not necessary to use a two-stage solution, it is often easier to understand the process in two stages. The first stage estimates the best-fit values of the flow rates. The second stage estimates the required component assay adjustments which are compatible with the best fit flow rates. The second stage of the process is also useful for Reconciliation to the mine or ore body where flow rates may be well defined and assays only defined by an ore body model. In this case, the adjustments are essentially an allocation of differences and they are not really a bestfit solution – simply a check on plausibility. The general problem looks quite daunting. It will first be defined and then a simple example will be worked through for illustrative purposes. The main aim of the balancing process is to achieve a set of self consistent stream flows and assays, which are in some way a best estimate of the true situation. Self consistent means that total ore and metal flows in and out of each node must be equal to within specified numerical criteria. Consider a flowsheet which has a number of streams indicated by subscript “i”. Some stream masses “M” are measured over a specified time interval and some streams are sampled and assayed for “j” assays “a” For this problem, three kinds of numbers can be defined: Actual assay and mass flow values which are perfectly self consistent but essentially unknowable M i * and a ij * . Measured values Mi for streams and aij for assays. These values are always inconsistent.

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Measured values have a measurement variance or standard deviation σ or SD associated with them. The glossary provides definitions as well as those for accuracy and precision. The adjusted data M i and aij may estimate the “true” value to within some defined

level. Adjusted data are the outcome of the balancing process. It is important to have a realistic measure of experimental variance or standard deviation for each measured value or Mi or aij . It is also important to eliminate (or at least measure) bias within ing balances. This topic will be considered later. The usual criteria for this method of analysis is to minimise the weighted sum of the squares of the difference between each measured and adjusted value divided by our estimate (standard deviation SD or σ ) of its accuracy. This value is usually called the weighted sum of squares WSSQ. There are two common ways to consider this problem. The simplest problem to solve is to consider the differences between flows of ore and metals in and out of each node. In mathematical minimise

⎛ M - Mi WSSQ = ∑ ⎜⎜ i i ⎝ σ Mi

⎞ ⎟⎟ ⎠

2

for mass flows

⎛ (Μ i aij − Μ i aij )2 ⎞ ⎟ for assay flows + ∑⎜ 2 2 ⎟ ⎜ Μ + a a σ Μ σ ij i i ij ⎠ ⎝ ij

subject to the constraint that the flowsheet must add up for total stream flows and component stream flows. After finding the “best” flows, the adjusted assays can be recalculated. Another standard approach is to minimise

⎛ aij − aij WSSQ = ∑ ⎜ ⎜ ij ⎝ σ aij

⎞ ⎟ ⎟ ⎠

2

where aij now refers to all measured mass flows and assays. As before, adjusted data must be self consistent. That is, it must satisfy the constraints imposed by the flowsheet network.

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There are many standard numerical methods which can be used to solve either variation of the problem. A simple example using the first approach is provided in Appendix E2. However more general solutions are beyond the scope of this note. A general approach developed by Morrison is detailed in Lynch, 1977 Chapter 7. Refer to Napier-Munn et al 1996, Chapter 5 for some worked examples. A more detailed description of the balancing process is given by Richardson,1991 and Richardson and Morrison, 2003 For our simple case, let us consider a separator which has a feed stream containing mass A over a specified time period and which produces a concentrate B and a tailing stream C. As noted earlier, three kinds of numbers can be defined for this problem:

•

Actual assay and mass flow values which are perfectly self consistent but essentially unknowable A*,B* and C* and ai* , bi* and ci*

•

Measured values A, B and C for streams and ai bi and ci assays. These values are always inconsistent. Each measured value has an associated measurement variance or standard deviation σ or SD. The measurement variance can be estimated by repeated measurement or repeated sampling and assay.

•

Adjusted data A, B and C and a, b and c which may estimate the “true” value to within some defined level. Adjusted data is the outcome of the balancing process. A

B C

The example process is a flotation bank. Each stream is sampled and assayed for several metals. These assays are designated ai, bi and ci, where i represents one of n components. For any given feed-rate, the product flows can be ratioed to the feed rate to simplify the calculation. 1

β

1-β For perfect data:

ai* = β bi* + (1 − β )ci*

(

which implies that β = ai* − ci*

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* i

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This is the basis of the well-known two-product formula. It is worth noting that even for perfect data, there is no useful balance around a splitter (or a separator which does not separate) where a=b=c and then β is equal to zero divided by zero which is undefined and can take any value. For measured data, adding a component flow error ∆ balances the equation for a given value of β

∆i = ai − β bi − (1 − β ) ci To simplify the calculation, assume the standard deviation estimate (SD) for each ∆ is one. Then the weighted sum of squares is:

SSQ = Σ∆i2 = Σ ( ai − β bi − (1 − β ) ci )

2

If an estimate of SD is available for each component, it can be used to scale the error for each component (i.e. divide each error by its standard deviation) and it makes the answer a little more complicated, as is addressed later. To find the minimum sum of squares, take the derivative of the SSQ with respect to beta and set the answer to zero.

∂SSQ / ∂β = 2Σ ( ai − β bi − (1 − β ) ci ) ( ai − ci ) which simplifies to

β = Σ ( ai − ci ) Σ ( bi − ci )( ai − ci ) 2

when the derivative is equal to zero. (Note that a splitter which works as a splitter still produces Σ0/Σ0 which can take any value.)

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E.2 NUMERICAL EXAMPLE Considering a simple numerical example may clarify some aspects. As β is the only variable, a direct search provides a way to consider dependencies in the solution. Figures 1 and 2 show variation in the sum of squares for β values from 0 to 1 in steps of 0.05 for the data shown in Table 1 below: Sample

Rougher Feed Rougher Conc Rougher Tail Beta A B C %Cu 2.92 6.9 1.9 %Pb 3.15 7.5 1.61 %Zn 6.6 27.4 2.7 %Fe 14.15 16.8 13.8 Table 1: β values for the component analyses

0.168 0.204 0.261 0.158 0.117

1000 100 10

%Cu %Pb %Zn %Fe SSQ

SSQ

1 0.1 0.01 0.001 0.0001 Beta 0-1

Figure 1: Sum of Squares vs β Values: Log axis 500 450 400 350 %Cu %Pb %Zn %Fe SSQ

SSQ

300 250 200 150 100 50 0 Beta 0-1

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The minima are well defined for each assay but they do not lie at the same value or at the best-fit value of 0.168 from the equation above. However, no matter what SDs are applied, the best fit value cannot move outside the range covered by these minima. Clearly, if all of the estimates of beta β are essentially the same, the mass split is well defined. In mathematical , how well β is defined depends on how sharply the SSQ curves at the minima. Hence, the assay (Zn) with the largest difference between feed and products and the most accurate data (smallest SDs) will dominate the combined results and produce the best estimate. If the separation process encomes the complete plant, the assay differences will be larger than for any single separation and they will be better defined. In short, even if the overall circuit balance is well defined, the internal balances may be poorly, or not at all, defined. It is clearly shown in Figure 1 that the smaller the SSQ at the minima, the sharper the curvature and the better the flows are defined. Data Adjustment Given a “best” set of flow rates, the next step is to allocate the flow component errors. As in the first stage, minimising the SSQ of the adjustments is a useful strategy. For each assay at the best value of β , there is a mass flow error:

∆ i = ai − β bi − (1 − β )ci If the small change required in each assay is defined as ∆ai , ∆bi and so on, then

0 = ( a − ∆ai ) − β ( b − ∆bi ) − (1 − β )( c − ∆ci ) and by difference

∆ i = ∆ai − β∆bi − (1 − β )∆ci The objective function for each assay is

SSQ = ∆ai2 + ∆bi2 + ∆ci2 subject to the constraint that the adjustments add up to the required component error. A standard technique to add a constraint to a minimisation is to use a Lagrange multiplier λ for each constraint. The constraint equation must be arranged to equal zero. Hence the modified sum of squares becomes:

SSQ = ∆ai2 + ∆bi2 + ∆ci2 + 2λ ( ∆ai − β∆bi − (1 − β )∆ci ) To find the minimum values, take the derivatives with respect to each adjustment and the Lagrange multiplier and equate to zero. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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∂SSQ ∂∆ai = 2∆ai + 2λ = 0 ∂SSQ ∂∆bi = 2∆bi + 2λβ = 0

∂SSQ ∂∆ci = 2∆ci + 2λ (1 − β ) = 0 ∂ SSQ ∂ λ = 2 ( −∆ai + β∆bi + (1 − β )∆ci ) = 0 This yields four equations in four unknowns.

∆ai = −λ ∆bi = λβ

∆ci = λ (1 − β ) and substituting for each adjustment yields

(

λ = −∆i 1 + β 2 + (1 − β )

2

)

This implies that apportioning the error according to the flow rate is the least squares solution when the SDs for each measurement are equal. This is intuitively reasonable. If each assay has its own SD estimate, the algebra is a bit more cluttered but not very different and the assay with the smallest SD is adjusted least. Table 2 shows a set of adjustments at the best-fit mass split of 16.8% to concentrate. This method gives a set of adjustments for any value of mass split. Therefore, the numerical derivative of β with respect to SSQ can be taken and iterated to the best solution. This solution will usually be close to the simply weighted analytical solution detailed above. If the data is accurate, the minima will closely coincide and the two methods will provide essentially identical solutions. In this case the solution will be insensitive to different SD estimates. As noted earlier, an estimate of the curvature at the minima of β provides an estimate of how well the flow split is defined. The sum of all of the adjustments squared in this case is 0.25 There are twelve measurements and four constraints in this case. Following the comments in Morrison, Gu and McCallum (2002), an expected value of the SSQ with good SD estimates would be 8 (i.e. 12–4). Therefore, the measured data in this case probably has much smaller SDs than the assumed value of unity for each. A standard way to present this type of data is measured data (flows, assays or component flows) against adjusted data. If most of the points lie along the diagonal from zero, then the data and the adjustment procedure and probably working well. All in all, the method of weighted least squares applied to the mass balancing problem provides a great deal of useful information.

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Table 2: Adjustments for Assay Data Component

Feed

Tails

Concentrate

Adj. A, λ

Adj. B, λ

Adj.C, λ

Weighted Sum Adjustment

% Cu

-0.104523

0.017564

0.086959

-0.179821

% Pb

-0.319849

0.053746

0.266103

-0.550269

% Zn

0.145597

-0.024466

-0.121132

0.250486

% Fe

0.089577

-0.015052

-0.074524

0.154108

Including measurement SDs in a least squares solution. The weighted least squares case is a little more complex but follows the same path as the data adjustment method. If it is assumed that any measurement biases are small, then the required data adjustments should be drawn from the probability distribution of variation associated with each measurement as are the experimental or measured values – or at least a very similar distribution. Therefore, on average, it can be expected that the weighted adjustments ∆ ai σ ai , ∆ bi σ bi etc, have an average value of about one and the weighted sum of squares (WSSQ) of adjustments have a value approximating the number of measurements. Hence, the magnitude of each weighted adjustment provides a good indication of data consistency at each point. The weighted sum of squares for each assay adjustment is:

WSSQ = (δ ai σ ai ) + (δ bi σ bi ) + (δ ci σ ci ) 2

2

2

subject to exactly the same constraint as the unweighted case. That is

2λ ( −∆ i + ∆ai − β∆bi − (1 − β ) ∆ci ) is added to the sum of squares and minimise with respect to λ as well. The derivatives are also quite similar. The adjustment is scaled by dividing it by the SD.

∆ai = λσ ai2 ∆bi = λβσ bi2 ∆ci = λ (1 − β )σ ci2 The derivative with respect to the La Grange multiplier stays the same but the scaled values are substituted back in to calculate it.

(

λ = − ∆i σ ai2 + βσ bi2 + (1 − β ) σ ci2 2

)

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NB the denominator for the LaGrange multiplier turns out to be an estimate of the standard deviation (SD) for ∆ i Therefore, the mass flow error for each measured component can be weighted and iterated to a weighted best fit value of beta. If the data is even slightly reasonable, the unweighted estimate of beta cn be used as a starting point and iterated to a stable solution. Obviously the component with the largest change in value (between con and tail) and the lowest relative SD for ∆ i will tend have a flow split very close to the best fit weighted beta. If the mass has been measured for more than one stream, a weighted stream adjustment can also be added to the weighted sum of squares. If only measured one stream has been measured, the flow ratio can be used. Even for this simple case, the analytical solution is quite complex and it is much easier to use MS Excel and Solver to find the best values. In this case the weighted SSQ for all components plus flows can be added together, if appropriate. The objective for Solver to minimise is the total WSSQ. There is only one flow split β but there is a λ for each constraint to identify as variables for Solver to adjust to achieve the minimisation. Evaluating the weighted balance The important advantage gained by this additional complexity is that the expected value of each adjustment is simply “one”. If the required adjustment is less than plus or minus one SD, then there is a two thirds chance that the adjustment is within measurement variation. If the adjustment lies outside plus or minus two SDs, then there is very likely to be a problem in the data and that adjusted data point is where to start looking at measurement or assay. However, if the adjustments all lie within plus or minus two SDs, the adjusted balance is probably as good as can be expected – subject to the checks for bias outlined next. If the measurement and balancing process were to be repeated many times, the adjusted data can be expected to exhibit a lower variance than that of the measured data. For some examples see Deglon and Chakraborty, (2005). Hence, the adjusted data can be compared with those to be used any Check inCheck out strategy. If the data adjustments for the streams and masses which are part commercial transfers, the Check in-Check out values can be assigned a very low variance and the weighted adjustment procedure run again to derive a set of data which are both technically self consistent and consistent with commercial value transfers. This approach can also integrate the primary and secondary data strategy used by some precious metal operations.

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Detecting Bias One of the disadvantages of using least squares adjusted data is that the adjustment process can make some types of bias more difficult to detect. However, it helps to identify some other types. In general, the detection of bias requires examination of a number of ing periods. The classical approach is compare cumulative sums (or cusums) of masses and component masses entering and leaving the system. Over an extended period (weeks to months) the contribution made by a bias will grow while the random errors should be both positive and negative and tend to cancel out as the sum accumulates. Calculating cusums monthly or quarterly should be a standard component of any metal ing strategy. However, they are always “after the fact”. The least squares analysis can often be used (if there is enough redundant data) as a problem detector over a shorter time frame. For example, if the multi-assay based mass split estimate is highly self consistent and does not match the measured mass split, then each mass measurement system needs to be checked. Mass measurement systems can very easily acquire bias due to a build up of material on a weigh frame or a simple fault (such as idler wheel slip) in belt speed measurement. Allowing a sample to lose moisture between collection and measurement is another way to introduce a bias. For multiple mass or multiple stream cases, in any single data set the data adjustment procedure will distribute both random variation and bias in one measurement across others of the same type which share the same constraint. However, over several data sets the biased measurement can be expected to require more adjustment in a particular direction than measurements which only exhibit random variation. In an unbiased system (where the measurement distribution is also symmetrical), the arithmetic sum of the weighted adjustments should be close to zero for each set and approximate zero when any selected data point is considered across multiple data periods. This is most easily checked graphically as many adjustment can be examined using a single trend graph. A yet more sophisticated technique is to add a “fittable” bias to a suspect measurement and repeat the analysis process across a sequence of data sets. This technique is plausible for one suspect measurement but applying it to several measurements can easily lead to serious self deception. To be detectable at all, a bias must persist long enough for its accumulated effect to be larger than measurement or balanced data variance. A so called “variable bias” is a system measurement problem and is not correctable by any mathematical technique – the underlying measurement problems simply must be rectified. The best way to guard against “variable bias” is for the person responsible for metal to visually inspect each mass measurement and sample point at least several times per week - daily if possible. Mathematical massaging can only conceal these problems when many of them are very easy for a trained eye to “see”. AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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In Conclusion These methods provide a powerful way to analyse metal ing data and to detect certain problems. The adjusted data provide a self consistent set which can be used to calculate key performance indicators such as metal recovery in a consistent manner – no matter which adjusted data is selected as the basis of the calculation. A second stage of adjustment can be used to align commercial and technical data – when and only when the data have been fully analysed and the hypothesis that the only variation is statistical is well ed. This technique also allows data from many sources within a processing system to be integrated for performance analysis and reporting. These methods are powerful but should be ed by cusums of total mass and key components to detect bias. Last but not least, regular physical examination of each measurement and sample point will detect many problems that mathematical techniques will not.

RICHARDSON, J. M. and MORRISON, R. D. (2003). Metallurgical Balances and Efficiency. Principles of Mineral Processing. M. C. Fuerstenau and Han, K. N. E. pp 363-389, SME, Littleton , Colorado, USA. Chakraborty A and Deglon D, 2006. Measurement Network Design For Metallurgical ing: A Heuristic Methodology. In Proceedings MPMSC Conference, Sudbury 6–7 June. Bepswa, P, Deglon, DA and Chakraborty, A, 2006. Heuristic methodology for deg measurement networks for metallurgical ing. In Proceedings of XXIII International Minerals Processing Congress, Istanbul, 3-8 September

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E.3 SAMPLING, METALLURGICAL ING AND REDUCTION OF BALANCE ESTIMATION VARIANCE GJ Lyman, AusIMM, Sampling and Blending Conference, Sunshine Coast, QLD 9-12 May 2005

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Sampling, Metallurgical ing and Reduction of Balance Estimation Variance G J Lyman1 ABSTRACT

INTRODUCTION

A metallurgical balance drawn around a mineral processing plant or smelter must involve all process streams crossing the plant boundary. Modern sampling theory, as formulated by Gy, and sound statistical method can be used to determine the uncertainties in the observed assays for the set of balance analytes and in the mass flow rates of the process streams for the balance period. These uncertainties can then be used together with the material balance equations to arrive at adjusted component mass flows that satisfy the material balances exactly. To determine whether or not the data is consistent with the level of uncertainty in the data and with the material balance equations, a goodness of fit test is applied. This goodness of fit criterion is the principal tool to quantify the quality of the material balances. The mathematical structure of the statistical material balance problem poses a number of challenges. For example, while the variances of the adjusted component mass flows do not vanish, the variances of the sums of the adjusted component mass flows about each balance node do vanish, simply because the balancing computation demands that the balances close exactly. Even though it can be shown that the variances of the adjusted component mass flows are always lower than the estimates for the corresponding unadjusted component flows, it is still not possible to construct a confidence interval on the component mass flow into (and out of) a balance node using the adjusted component flows. On the face of it, such a confidence interval is exactly what is most desired from the balance calculations. The problem arises because the balance problem is a data adjustment rather than a parameter estimation problem. It is possible, however, to calculate the recovery of a component to a product stream and determine a confidence interval for the recovery value. It is also possible to calculate an uncertainty in the mass flow of any balance species in any process stream, before and after balancing. Balance results have little meaning without the confidence interval estimates. Having invested in the manpower and procedures that make a statistically valid balance on an operation possible, the management may find that the levels of uncertainty associated with the balances are rather higher than they would like. The obvious solution to the wide confidence intervals is to reduce the uncertainty in the assays and mass flow rates on the principal process streams. The cost of this uncertainty reduction may be quite significant, as it will involve reduction in analytical variances or reduction in sampling and preparation variances which may require investment in and development of new sampling hardware or routine duplication of assays to reduce the assaying variance. A second strategy for uncertainty reduction can be proposed which involves the use of additional sampling points to provide material balance nodes internal to the processing operation. The information required to make these internal balances may well exist within the operation, but it may not be recognised as being of value or the standard of sampling and assaying may not be deemed to be sufficient to use the data for metallurgical ing purposes. The marginal cost of elevating the existing sampling systems to metallurgical ing standards may well be smaller than that to improve the sampling and assaying on the boundary streams. This paper presents a statistical analysis of an uncomplicated form of the balance calculations that permits exploration of accuracy issues and the advantages that come from additional internal balancing nodes for the process flow sheet. The calculations are based on a relatively simple flow sheet of common structure, but are of general applicability. The objective is to reveal broad issues. While each process flow sheet and sampling and analysis system requires individual analysis to reveal precisely where the gains can be made most economically, a general result can be demonstrated which provides an estimate of the limit to the variance reduction which can be achieved by adding internal balance nodes.

Large metallurgical processing and refining operations are increasingly concerned with the measurement of their performance. The availability of powerful on-line measurement systems, cheap networked computing power and the ability to manage large databases of information collected across the operation make it possible to construct systems that a manager can use to view and analyse any part of the operations at a range of scales or levels. The determination of a material balance around an operation is the most fundamental calculation that can be carried out, but balances and recovery estimates are subject to uncertainties that derive from uncertainties in the measured assays and mass flows. For high value materials, balances with the corresponding statistical analysis may also be used to evaluate the possibility that theft of material from the process has taken place, if all the measurement processes are well-understood and controlled throughout the balance period. The method of calculating a balance around a particular metallurgical operation is usually one that has evolved over time. It is common to find that the balance calculations rely on ‘assumptions’ about the characteristics of some of the process streams or stock levels within the process. The common origin of the assumptions is a missing or unreliable sample for the particular process streams or a mass flow measurement that is considered unreliable, that is, of high variance. For example, the moisture content determination of the material may be deemed unreliable and therefore an ‘historically determined’ or ‘agreed’ figure for the moisture is used in the calculations. The impact of the assumption is usually unknown. The traditional methods of balancing also tend to rely upon or focus upon a single chemical or physical species in the calculations. There are generally two reasons for this. First, the target species is the one of primary value and second, the use of a single species avoids the disconcerting fact that balances based on additional species may provide different results and the technically appropriate method of reconciling the results of the disparate balances is not known to the staff of the organisation. If an operation has developed a corporate and economic justification for carrying out technically correct material balances on an operation, it is necessary to review all sources of data that are used in the balance calculations and then eliminate the ‘assumptions’ involved and replace them with statistically sound statements of the uncertainties involved in the measured quantities. The review of the data must scrutinise all sampling and analytical procedures that are used to acquire information and develop realistic descriptions of the uncertainties involved in all measurements. Ideally, the total variance will be divided into two components, the first due to due sampling and preparation and the second due to analysis. It is usually necessary to review the operation of the monitoring systems that are in place to ensure that sampling systems functioned as required and that laboratory quality control systems remained in place during the balance period. Ensuring that a large analytical facility carrying out diverse assays operates within specification accuracy on a routine basis is a great challenge that demands the goodwill, cooperation and diligence of a large team of skilled persons.

1.

Sampling Consultant, 17 Diosma Street, Bellbowrie Qld 4070.

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G J LYMAN

Competently carried out, the review will usually identify effective means of actually increasing the amount of information that can be extracted by the balances. The traditional view of a mechanical sampler defines it as a device for acquiring a representative sample of the material flowing past it; the sample is sent for analysis to determine the content of various analytes of interest and the analyte contents are used in the balance calculations. However, as Gy has pointed out many times, a correct mechanical sampler will also operate in such a way as to provide a sample whose mass is proportional to (that is, representative of) the total mass of material that ed the sampling point during the period of sampling. The use of mechanically correct sampling equipment to determine mass flow estimates is a means of acquiring information that continues to be grossly overlooked. The review of the sampling systems in place and the data available for balance calculation has to be linked to a consideration of how the balances should be calculated. In the simplest case, where all inputs, outputs and changes in stock levels are to be reconciled over an ing period (month or quarter, say) the structure of the balance is fixed and it remains to decide which species will be used in the balance and whether or not certain mass flows will be computationally estimated at the same time as the mass flow rates and/or assays are adjusted to provide an exact closure of the balance. It would be unusual to find that all the sampling systems and data monitoring procedures needed to provide the data for a complete and statistically sound balance were already in place. It would also be surprising if all systems provided data with uncertainty levels that were deemed acceptable. Most operations have no quantified knowledge of the accuracy of their sampling and assaying; the determination of the accuracy of sampling is a prerequisite to deciding how to best use the data that arises from the sampling. An understanding of the constitutional and distributional heterogeneity of all process streams at the range of levels of comminution involved in the sampling and analysis for all analytes of interest is not common. Faced with the problem of enlarging the sampling system and doing something to decrease the levels of uncertainty in the data, the best way of investing in equipment or additional analyses may not be immediately obvious. For some process streams, there will be little choice; a new sampler will have to be installed. However, the sampling frequency to be employed for that stream will dictate the accumulated primary increment masses and the need for more or less expensive sample handling equipment and secondary or tertiary sample division at the sample point. The sampling frequency used will also control the variance component due to distributional heterogeneity (DH), and, if the material sampled has a high intrinsic (constitutional) heterogeneity with respect to analytes of interest, the variance due to intrinsic heterogeneity (IH) may be important as well. Statistical material balance (SMB) methods, in which all available data are used and are weighted in the calculations according to their accuracy, have two advantages over traditional ad hoc balance calculations. First, the method provides a balance that closes exactly for all species and mass flows involved and second, SMB permits the statistical interpretation of the data and the results of the calculations. In particular, it is possible to determine confidence intervals or even full statistical distributions for computed results, where the variables of interest have not had their variance suppressed by the balance calculations. It is possible to quantify, for example, the probability that the output of valuable exceeded the balance value by a given number of grams, ounces or tonnes. It is also possible to quantify the probability that the recovery of SO2 to the scrubbers fell below a target figure.

128

While the theory behind SMB that permits the optimal estimation of balances can become very complex in a general case and particularly when some uncertainties in assays or mass flows do not follow Gaussian (normal) distributions, it can be quite simple in some cases and can be used to very great advantage to estimate how accurate balances will be, given the level of uncertainty in the data going into the balance and the structure of the balance. It can therefore be used to investigate alternate balance structures and data quality scenarios and the quality of the information that will arise from the balance. The theory behind SMB in fact provides a design tool for sampling and material balance networks. A metallurgical operation would certainly not invest in a new process without making substantial efforts to determine the quality of product it will ultimately provide. However, operations often construct sampling and material balance systems without an appropriate understanding of the quality of the data that will be produced. This paper has the objective of showing how the simplest SMB theory can be used to estimate the quality of the balance results that will be obtained, prior to any final decision on a sampling system and material balancing system design. A simple case is studied in which all uncertainties are assumed to follow Gaussian distributions. The scenario is one in which an overall balance on an operation is needed, but an option exists to include internal process balances in the SMB calculations. The statistical advantage that can be obtained by including the internal balances in the calculations is quantified. This scenario is one that is accessible to many operations and the marginal cost of reducing sampling variance for the perimeter streams may be far higher than making relatively simple changes in procedures and taking advantage of samplers that already exist for the internal process streams. The flow sheet chosen for consideration is also a simple one but one that is common to flotation as well as smelting operations. Structurally, it corresponds to the flow sheet that was employed at Bougainville Copper in their flotation circuit (Hinckfuss, 1976) and is also a common smelting flow sheet in which final slags are retreated to recover entrained values. The flow sheet is shown in Figure 1 together with the stream numbers, process deportments and nominal balance data. For a smelting flow sheet, stage 1 corresponds to a furnace producing a matte and slag, stage 2 corresponds to a converting process and stage 3 to a converter slag scavenging process, recycling a matte material to the converters. The mass flow basis is 100 units of mass input. In order to keep the problem simple, internal accumulation in the process has been ignored. Stock build-up or drawdown can have a dominant impact on balance variances when not determined with adequate accuracy.

STATISTICAL DEVELOPMENT Data model The development of a statistical model of the data is the first step in any statistical analysis problem. The problem considered here is taken as one in which the processing operation receives consignments of feed material on a daily basis and a daily composite sample is prepared for assay. Daily dry solids inputs are determined from a wet tonnage delivered and a moisture determination on the as-received solids. Outputs are similarly determined, using daily or weekly composites or assays and weights of final product (stream 4) consignments. To keep this case simple, build-up or draw-down of internal stock of in-process material will be ignored. ing for these stocks would modify external flows and add some internal flows. At the end of the month, the net input of a species is a sum over the periodic inputs and the net output is a similar sum over outputs.

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Stage 1 (Rougher) 1 Species 1

mass 1.000

conc 1.00%

2 3

11.000 12.000

11.00% 12.00%

4 5

13.000 5.000

13.00% 5.00%

58.000

58.00% 100.00%

6

100.000

Rec. to

98.00%

Conc.

80.00% 40.00%

3

2

10.00%

mass 0.020

conc 0.02%

3.00% 1.00%

2.200 7.200

2.64% 8.63%

11.700 4.850

14.03% 5.82%

57.420

68.86% 100.00%

mass 0.980

conc 5.90%

8.800 4.800

52.98% 28.90%

1.300

7.83%

0.150 0.580

0.90% 3.49%

16.610

100.00%

mass 1.086

conc 4.55%

mass 0.106

conc 1.46%

10.173 10.435

42.59% 43.68%

1.373 5.635

18.87% 77.43%

1.429 0.155

5.98% 0.65%

0.129 0.005

1.77% 0.06%

0.610

2.55% 100.00%

0.030

0.41% 100.00%

23.888

83.390

7

7.278

Stage 2 (Cleaner) Rec. to Conc.

4

Stage 3 (Cleaner Scavenger) 5

90.00% 85.00%

Conc.

40.00% 10.00%

mass 0.109

1.00% 2.00%

mass 0.978 8.647

conc 7.01% 61.96%

4.174

29.91%

0.143 0.002

1.02% 0.01%

0.012

0.09% 100.00%

13.956

Rec. to

98.00% 90.00%

6

conc 1.09%

90.00% 10.00%

1.526

15.36%

3.00%

0.153

5.75%

6.261 1.286

63.04% 12.95%

5.00%

0.626 1.157

23.59% 43.60%

0.153 0.598

1.54% 6.02%

0.148 0.568

5.59% 21.39%

9.932

100.00%

2.654

100.00%

mass 0.002

conc 0.08%

FIG 1 - Process flow sheet for balances, with process unit species deportments and balanced mass flows and concentrations.

When internal flows are also to be taken into , a similar ing procedure is common. For example, in a smelting circumstance, furnace matte would be sampled when tapped and then weighed as charged to the converters; if granulated, it can be readily sampled and weighed as it is conveyed to the holding bins ahead of the converters. In a flotation operation, the ideal means of determining the mass flows is proportional sampling of the slurry streams, with daily or shift samples for assay. For the sake of discussion, daily composites and monthly balances (30 days) will be assumed. This mode of material movement and sampling is modelled by measured masses of daily flows denoted by mij for stream j and day i. The total measured mass flow of the process stream j over the month is then Mj: Nj

M j = ∑ mij

(1)

i=1

where: Nj

is the number of daily lots involved for the stream

The measured assay for species k on day i and in stream j is denoted as aijk . The uncertainties in the mass flows are µ ij and in the assays, α kij . Sampling theory combined with knowledge of material IH and DH and testwork can be used to quantify the uncertainties. In the present circumstances, these uncertainties will be taken to have Gaussian distributions with zero mean (unbiased sampling and assaying). The statistical properties of the uncertainties are then completely described by the variance and covariance of the random quantities. Because a species mass flow for the month depends on both assays and daily lot masses, all monthly species mass flows within a given stream will be correlated and all species flows in that stream will correlate with the total mass flow. However, daily mass flow measurement uncertainties will not tend to correlate with assay uncertainties unless the process stream assay correlates strongly with the mass flow and the primary sampling frequency is lower than dictated by sound sampling system design. On the above basis, the monthly total and species mass flows can be written as: Nj

The total mass flow of species k over the month for stream j is given by:

~ +µ ) M j = ∑(m ij ij i=1

(3)

N

Nj

A kj = ∑ mij aijk

(2)

j ~ = M j + ∑ µ ij

i=1

i=1

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Nj

(4)

MkT is the transpose of a column vector of the flow of species k in process streams 1 to Ns

where mathematical symbols with a tilde (~) denote the true, and thus unknown, value of a flow or assay and Greek symbols denote discrepancies of observed values from true values. The variances of the monthly total and species mass flows are then:

A tilde will again be used to denote the true values, no mark will be used to denote the measured values and the statistically adjusted values will be denoted by a caret (^). To write the material balances concisely, an expanded version of the C matrix is needed. Construct an ( N c + 1)( N n ) × ( N c + 1)( N s ) matrix, where Nn is the number of balance nodes or rows of C, by locating copies of the C matrix down the diagonal and call this matrix G:

~ + µ )( a~ k + α k ) Aijk = ∑ ( m ij ij ij ij i=1

N

j ~ ~ α k + a~ kµ + µ α k ) = Aijk + ∑ ( m ij ij ij ij ij ij

i=1

{ }= N σ

Var M j

j

2 µj

(5)

and

{ }= N

Var A kj

j

( )σ

m 2 σ 2 + a k k j  j α j

2

2 µj

+ σ α2 k σ µ2 j  j 

(6)

{

Cov M j , A

}= N a σ j

k j

2 µj

0 0 0 C 0 0  0 O 0 0 0 C

(11)

The material balance that is to be satisfied is then:

and it has been assumed for convenience that all daily flows and assays can be taken to have average values m j and a jk without loss of significance of the calculations. The daily mass flows are assumed to have nominally equal variances of σ 2µ j and the assays similarly nominally equal variances of σ 2α k . This assumption is not j necessary; it simply makes the notation less cumbersome. The covariance between the total mass flow and the species mass flow is: k j

C 0 G= 0 0 

(7)

Gz$ = 0

(12)

The vector of adjusted mass flows is the sum of the measured flows and adjustments ξ: z$ = z + ξ

(13)

Note also that the true values must satisfy the material balance equations so that Gz~ = 0.

Method of determination of optimal adjustments Cov A , A

l j

}= N a a σ j

k j

l j

2 µj

(8)

Note that the species mass flows remain correlated even if a balance on total mass flow is not included in the calculations.

Material balance model The material balances are conveniently captured by a matrix C (connection matrix) which has number of rows equal to the number of material balance nodes and number of columns equal to the number of process streams involved in the balance. In each row, an element is one if the stream flows into the node and -1 if it flows out of the node. The elements are otherwise zero. The simple balance around the flow sheet of Figure 1 would first require renumbering of the streams from 1 to 4 (change 4 to 3 and 6 to 4). The C matrix for this case has one row with entries [1 – 1 – 1 – 1] whereas balances around each stage of the process (with stream numbering as on the Figure) has three rows:

… species 1

…

=

… …

(10)

total flows

… …

z T = [ M T M 1T M 2T K M N c T ]

…

(9)

To deal with all the material flows at once (total flow and species flows), it is convenient to adopt a more compact notation. All mass flows are put into a vector z such that:

species Nc

species 1

…

−1 −1 0 0 0 0  0 1 −1 −1 0 1   0 0 0 1 −1 −1

total flows

…

1 C = 0  0

The best method of determination of the adjustments to the mass flows is the method of maximum likelihood (ML). This method finds adjustments from knowledge of the probability distributions of the measurement uncertainties. The particular advantage of ML methods is that, when the data set is large enough (and this fact can be easily checked by Monte Carlo simulations of the data structure), the ML estimators are unbiased and have the lowest variance of any estimator that might be reasonably constructed. When all uncertainties in the data are normally distributed (Gaussian), the ML estimators are the same as the weighted least-squares estimators. Having assumed Gaussian data, the least squares solution to this adjustment problem is optimal. The next step is to construct the data covariance matrix Σ for all the process mass flows. Given the way that the vector z is constructed, the covariance matrix will be banded as illustrated figuratively in the next equation. Each submatrix has Ns rows and columns.

…

{

k j

…

and between the species mass flows is:

where:

…

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species Nc

…

MT is the transpose of a column vector of total process flows in streams 1 to Ns

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SAMPLING, METALLURGICAL ING AND REDUCTION OF BALANCE ESTIMATION VARIANCE

The weighted least squares formulation requires minimisation of the quadratic form ξ T Σ −1 ξ, subject to the requirement that Equation 12 be satisfied. It is also useful to note that:

therefore subject to less uncertainty than the measured values. For a scalar function of the adjusted flow rates, f ( z$ ), such as a recovery value, the variance of the function is, approximately:

δ = Gz

Var{ f } = ∇f T Σ a ∇f

(14)

is a vector of the extent to which the measured values fail to satisfy the material balance and is called the vector of node assay residuals. Equation 12 is then: Gξ = −δ

(15)

The minimisation of the quadratic form subject to the above linear constraints is solved by the method of Lagrange multipliers and has a linear solution. The solution is: ξ = − ΣG T (GΣG T )−1 δ

(16)

If the measurements are unbiased, this result is unbiased.

Statistical results It is not difficult to demonstrate that: Cov{δ} = E{δδ T } = (GΣG T )

(17)

The operator E{} denotes the statistical expectation of the quantity. Note that this covariance is that of the node assay residuals before data adjustment. The covariance of the node assay residuals after adjustment is identically equal to zero. The method of solution forces the adjusted node assay residuals to be zero regardless of the data and the outcome of every calculation is zero node assay residuals. The covariance matrix of the adjustments to the mass flows is: Cov{ξ} = E{ξξ T } = ΣG T (GΣG T )−1 GΣ

(18)

and the minimal value of the weighted sum of squares is: WSSQ = ξ T Σ −1 ξ = δ T (GΣG T )−1 δ

(19)

This quantity depends on the data and is therefore a random variable. It can be shown to have a chi-squared distribution with N n ( N c + 1) degrees of freedom. This fact can be used to carry out a test of goodness of fit of the data to the mass balance equations. If this test fails, it means that either the data do not obey the balance equations or the estimates of the measurement variances are incorrect. This test is the principal means by which to evaluate the quality of the data and the validity of the balance. The weighted sum of squares can be partitioned into components due to each species to determine the contribution of each analyte to the total. Finally, the covariance matrix of the adjusted mass flows is given by: Cov{z$} = Σ a = Σ − ΣG T (GΣG T )−1 GΣ

(20)

This equation shows that the variance of the adjusted mass flows must always be less than that of the measured flows as the matrix being subtracted is a positive definite covariance matrix. The fact that the covariance of the adjusted values is a simple difference between the covariance matrix of the observed values and the covariance of the adjustments is a consequence of their statistical independence which derives from the general structure of linear least-squares problems. The adjusted mass flows are

Sampling and Blending Conference

(21)

This expression is exact only if the function f is linear in the adjusted mass flows; ∇f is the gradient vector of the function f. Equation 20 shows that the covariance matrix depends only on the structure of the material balance as captured by the C matrix and the uncertainties in the measurements. The covariance of the results does not depend on the particular values of the measured mass flows as long as the covariance matrix of the measurements does not change greatly as the mass flows change. For a processing operation that does not have large variations in relative process species mass flows from one month to the next, the covariance matrix of the adjusted values is, to a first approximation, constant. This last result is very significant as it means that once realistic estimates of the uncertainties in the mass flow and assay measurements have been established, it is possible to study various balance configurations to see which configuration provides the greatest reduction in variance between the measured and adjusted mass flows. While the calculations above may seem to be daunting, they are actually trivially simple in comparison to the task of establishing the real measurement accuracies for the process streams. In the context of this paper, the above result will be used to study the impact of balance configuration and the influence of measurement accuracies in key process streams on the accuracy of adjusted species flow estimates.

CASE STUDY Data adjustment The figures used for the deportments of the species are entirely fictional but have been developed to include both ‘gangue’ or ‘slag components’ and valuables in the balances. Table 1 proves the deportments and comment on the nature of the species it is intended to represent. The species represent a range of deportments including one that is not well separated in stage 1 and 2. Species 6 is a mixture of elements for which no assay is determined; it can be regarded as the ‘balance of the sample’. The nominal measurement relative standard deviations for either total mass flow or analytes one to five within a daily sample are provided in Table 2. The figures given are percentages and are realistic in the author’s experience. To ensure that the estimate of the covariance matrix originates from a realistic data set, that is, one on which the balances do not close exactly the base data from Figure 1 were perturbed according to the monthly variances and covariances. The balance based on all seven streams and three balance nodes and the first five species leads to the variance estimates of Table 3. The inclusion of the all six species in the process stream would lead to a singular solution to the balance, as the sum of all species must always equal the total mass flow. To determine the mass flow of the last species, the five balance species are subtracted from the balanced total mass flow. To ensure that the estimate of the covariance matrix originates from a realistic data set, that is, one on which the balances do not close exactly the base data from Figure 1 were perturbed according to the monthly variances and covariances and when this perturbed data set was re-balanced, it was taken to be the true base case.

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131

G J LYMAN

TABLE 1 Species, deportments and comment. Species

Deportment to product

Comment

Stage 1

Stage 2

Stage 3

1

98

90

98

Primary valuable, well recovered to the product stream and closely associated with secondary valuable

2

80

85

90

Secondary valuable, quite well recovered to product

3

40

40

90

Neutral species not well separated in stage 1 and 2 but recovers well in cleaner scavenger

4

10

10

10

Low level gangue or slag component moderately rejected to waste

5

3

1

3

Minor gangue or slag component well rejected to waste

6

1

2

5

Major gangue or slag component strongly rejected to waste in stage 1 and 2

Estimation of component recovery TABLE 2 Relative standard deviations of daily mass flow and assay measurements. The tabulated figure is a percentage. Component

Stream 1

2

3

4

5

6

7

Total mass

2.5

5.0

3.0

2.2

2.1

5.1

3.2

1

6.1

10.

5.1

4.1

6.0

12.

6.0

2

5.2

6.1

4.1

4.0

5.1

5.0

5.0

3

5.9

5.9

4.9

5.1

5.9

4.0

5.0

4

3.1

3.1

3.0

5.9

6.0

3.1

3.1

5

5.1

4.9

6.1

7.0

5.0

5.1

6.9

The results of perturbing a base set of data by adding appropriately correlated random components of a prescribed covariance structure and re-balancing the data in a least squares sense are provided in Table 3. The perturbed values are called ‘measured values’. The full covariance matrices for each stream are not provided. Instead, the square root of the diagonal elements of Σ, the standard deviations of the measured component flows (SDM) are provided both as absolute values and relative values (RSD as a per cent) are given. At the same time, the calculated standard deviations of the adjusted assays (SDA) can be determined from Equation 20 and tabulated as well. The last column of Table 3 provides the ratio of the variance of the adjusted flow to that of the unadjusted component flow. This figure is the measure of decrease in the component flow uncertainty that can be attached to the mass balancing procedure. Ideally a balance procedure would reduce this ratio to zero but, here, the average ratio is ~0.5. Note finally that the WSSQ value for the adjustment is 14.2, with an expected value of 18, which leaves no doubt that the material balance equations are satisfied. Table 4 provides the corresponding results for a single overall balance. The adjusted component flows for the perimeter streams are essentially identical. Note that the stream numbering has changed: change four to three and six to four. However, in this case the reduction in variance from the measured uncertainties to the balanced component flows is only 30 per cent rather than 50 per cent. While the variance reduction has been calculated from theory, a Monte Carlo study involving the repetitive perturbation of a base data set followed by adjustment of each of the data sets would lead to precisely the same result in the variance reduction. Such an exercise is not difficult but is unnecessary in a case of weighted least-squared data adjustment, which is an entirely linear problem.

132

Because the balance is calculated in of the component mass flows, the calculation of a recovery figure is simply the ratio of the mass flow in the product stream to that in the incoming stream and the uncertainty in the recovery can be determined by application of Equation 21. For the balance with internal nodes, stream 4 is the product stream carrying the species of primary interest (species 1). Species 2 is also of interest and species 5 is of interest in of its recovery to waste streams. Table 5 provides the recoveries and their uncertainties for the species and streams noted. The recovery values for both balance types are very similar, and in all cases the standard deviation of the recovery estimate is smaller when the three node balance is used. Only in two cases do the extra nodes in the balance substantially decrease the uncertainty in the recovery values. The recovery of the primary valuable species (1) to product (stream 4 or 3) is determined with apparent great accuracy and the accuracy does not improve very much at all when the more complex balance is used. It is only the gangue (5) recoveries to waste (stream 2) and to product (stream 6) that benefit significantly from the more complex balance. However, it is important to realise that it is not the accuracy of the recovery that is the critical quantity. If the mass flow of the component in the feed stream were known with absolute certainty, the recovery uncertainty would be the critical figure. But the feed stream mass flow carries an uncertainty, so the quantity of interest to the metallurgist is actually the uncertainty in the mass flow in the product stream. For species 1, this uncertainty for the overall and three node balance are found within Tables 4 and 3. In both cases, the RSD for the measured flow in the product stream (3 or 4) is the same, but the adjusted mass flows show RSDs of 0.37 per cent and 0.30 per cent for the one node and three node balances, respectively. This represents a 20 per cent reduction in SD or a 36 per cent reduction in variance.

Sensitivity of results to measurement uncertainty and flow sheet structure To determine the sensitivity of the balance to the accuracy of the measured values, consider a doubling of the standard deviation of the all measurements on the internal streams. When the full three node balance is carried out, one might expect the balance to be degraded by the low precision data. On average, this is what takes place, as the average variance reduction remains as it was for the initial balance at about 50 per cent (Table 3; average variance ratio). Figure 2 plots the variance reduction ratios that arise as a result of the balancing process. It is clear that some variances are degraded while others are improved. However, the average variance reduction ratio, as previously stated, remains the much the same.

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TABLE 3 Results for least squares adjustment of component flows with variances using three balance nodes. The WSSQ value is 14.2 and the average variance ratio is 0.49. Stream

Flows

Measured

Adjustment

Adjusted

SDM

RSD (%)

SDA

RSD (%)

Var ratio

1

mass

99.5172900

0.3584135

99.8757040

0.4542322

0.45

0.3624343

0.36

0.64

1

0.9975235

0.0070499

1.0045734

0.0120095

1.20

0.0049690

0.49

0.17

2

10.8411280

0.1105074

10.9516350

0.1142302

1.04

0.0533312

0.49

0.22

3

11.9086810

0.1365811

12.0452620

0.1393566

1.16

0.0794091

0.66

0.32

4

12.9694230

0.0134608

12.9828840

0.0943178

0.73

0.0694370

0.53

0.54

5

4.9862954

0.0143167

5.0006122

0.0517201

1.03

0.0373300

0.75

0.52

mass

83.6325490

-0.3799723

83.2525770

0.7634572

0.92

0.3581939

0.43

0.22

1

0.0208311

-0.0000987

0.0207324

0.0004256

2.05

0.0003917

1.89

0.85

2

2.2212457

-0.0142280

2.2070177

0.0320104

1.45

0.0265272

1.20

0.69

3

7.3236451

-0.0687750

7.2548701

0.1034831

1.43

0.0759617

1.05

0.54

4

11.7113400

-0.0260468

11.6852930

0.1258338

1.08

0.0692866

0.59

0.30

5

4.8689083

-0.0189101

4.8499982

0.0622700

1.28

0.0373225

0.77

0.36

mass

16.6528370

-0.0297098

16.6231270

0.0912113

0.55

0.0465734

0.28

0.26

1

0.9848403

-0.0009993

0.9838410

0.0106426

1.08

0.0049480

0.50

0.22

2

8.8081249

-0.0635076

8.7446173

0.0817230

0.93

0.0475216

0.54

0.34

3

4.8061904

-0.0157982

4.7903922

0.0504319

1.05

0.0313239

0.65

0.39

4

1.3066553

-0.0090642

1.2975910

0.0101236

0.78

0.0063122

0.49

0.39

5

0.1480575

0.0025565

0.1506140

0.0018382

1.22

0.0009678

0.64

0.28

mass

13.9745220

-0.0129900

13.9615320

0.0561305

0.40

0.0425430

0.30

0.57

1

0.9835847

-0.0019182

0.9816665

0.0065377

0.67

0.0049468

0.50

0.57

2

8.5839360

0.0087002

8.5926362

0.0715575

0.83

0.0474234

0.55

0.44

3

4.1633568

-0.0065558

4.1568010

0.0422279

1.02

0.0309195

0.74

0.54

4

0.1399171

0.0001824

0.1400995

0.0016089

1.15

0.0015704

1.12

0.95 0.96

2

3

4

5

6

7

5

0.0015772

-0.0000019

0.0015754

0.0000211

1.34

0.0000207

1.32

mass

9.9287668

0.0139440

9.9427109

0.0380675

0.38

0.0287047

0.29

0.57

1

0.1082153

-0.0007784

0.1074368

0.0012562

1.17

0.0008919

0.83

0.50

2

1.5189549

0.0074900

1.5264450

0.0152984

1.00

0.0105447

0.69

0.48

3

6.3229750

-0.0363283

6.2866467

0.0723102

1.15

0.0455585

0.72

0.40

4

1.2664532

0.0207463

1.2871995

0.0147014

1.14

0.0062253

0.48

0.18

5

0.1537728

-0.0001663

0.1536065

0.0015228

0.99

0.0009687

0.63

0.40

mass

2.6890580

-0.0274629

2.6615950

0.0250386

0.94

0.0151470

0.57

0.37

1

0.0021955

-0.0000210

0.0021745

0.0000523

2.41

0.0000497

2.29

0.90

2

0.1535941

-0.0016130

0.1519811

0.0020041

1.32

0.0016511

1.09

0.68

3

0.6399527

-0.0063615

0.6335911

0.0075767

1.20

0.0059033

0.93

0.61

4

1.1677094

-0.0102179

1.1574915

0.0127284

1.10

0.0062073

0.54

0.24

5

0.1522891

-0.0032505

0.1490386

0.0020067

1.35

0.0009669

0.65

0.23

mass

7.2794283

0.0016875

7.2811158

0.0425291

0.58

0.0274504

0.38

0.42

1

0.1043722

0.0008902

0.1052624

0.0012963

1.23

0.0008917

0.85

0.47

2

1.3783868

-0.0039229

1.3744639

0.0149447

1.09

0.0105225

0.77

0.50

3

5.6260314

0.0270241

5.6530556

0.0609982

1.08

0.0454820

0.80

0.56

4

0.1297311

-0.0000231

0.1297081

0.0010555

0.81

0.0008866

0.68

0.71

5

0.0045662

0.0000017

0.0045679

0.0000634

1.39

0.0000602

1.32

0.90

It is of interest to determine whether or not there is some general statement that can be made about the improvement in variance that can be achieved by introducing additional balance streams and nodes. The ratio that has been calculated is the ratio of the variance of the adjusted component flow to its measured variance. In general , a ratio of the nature: Σ R= a Σ

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is of interest. Recognising that the expressions involve matrices and using Equation 20, the quantity of interest is: R = Σ −1 [ Σ − ΣG T (GΣG T )−1 GΣ ] = I − G T (GΣG T )−1 GΣ To simplify the structure of the uncertainties, let it be assumed that all measurement uncertainties are statistically independent. In such a case, the matrix Σ is diagonal. Then the ratio of the

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133

G J LYMAN

TABLE 4 Results for least squares adjustment of component flows with variances using a single balance node. The WSSQ value is 2.4 (expected value 6) and the average variance ratio is 0.70. Stream

Flows

Measured

Adjustment

Adjusted

SDM

RSD (%)

SDA

RSD (%)

1

mass

99.5172900

0.3239744

99.8412650

0.4542322

0.45

0.3664377

0.37

0.65

1

0.9975235

0.0066914

1.0042149

0.0120095

1.20

0.0057209

0.57

0.23

2

10.8411280

0.0755441

10.9166720

0.1142302

1.05

0.0644001

0.59

0.32

3

11.9086810

0.1305750

12.0392560

0.1393566

1.16

0.0820157

0.68

0.35

4

12.9694230

0.0128725

12.9822950

0.0943178

0.73

0.0698344

0.54

0.55

5

4.9862954

0.0135906

4.9998861

0.0517201

1.03

0.0374113

0.75

0.52

mass

83.6325490

-0.4331022

83.1994470

0.7634572

0.92

0.3609159

0.43

0.22

1

0.0208311

-0.0001119

0.0207191

0.0004256

2.05

0.0003919

1.89

0.85

2

2.2212457

-0.0138327

2.2074130

0.0320104

1.45

0.0265553

1.20

0.69

3

7.3236451

-0.0727133

7.2509318

0.1034831

1.43

0.0763607

1.05

0.54

4

11.7113400

-0.0366727

11.6746670

0.1258338

1.08

0.0698590

0.60

0.31

5

4.8689083

-0.0228845

4.8460237

0.0622700

1.28

0.0374251

0.77

0.36

mass

13.9745150

-0.0216839

13.9528310

0.0561305

0.40

0.0522713

0.37

0.87

1

0.9835833

-0.0022829

0.9813004

0.0065377

0.67

0.0057010

0.58

0.76

2

8.5839255

-0.0282493

8.5556762

0.0715574

0.84

0.0602064

0.70

0.71

3

4.1633542

-0.0148434

4.1485108

0.0422278

1.02

0.0404544

0.98

0.92

4

0.1399166

-0.0002047

0.1397119

0.0016089

1.15

0.0015961

1.14

0.98

2

3

4

Var ratio

5

0.0015772

-0.0000024

0.0015748

0.0000211

1.34

0.0000210

1.33

0.99

mass

2.6890580

-0.0000715

2.6889865

0.0250386

0.93

0.0249522

0.93

0.99

1

0.0021955

-0.0000001

0.0021953

0.0000523

2.38

0.0000523

2.38

1.00

2

0.1535941

-0.0000116

0.1535825

0.0020041

1.30

0.0020005

1.30

1.00

3

0.6399527

-0.0001391

0.6398136

0.0075767

1.18

0.0075567

1.18

0.99

4

1.1677094

0.0002074

1.1679167

0.0127284

1.09

0.0126607

1.08

0.99

5

0.1522891

-0.0000016

0.1522876

0.0020067

1.32

0.0020030

1.32

1.00

TABLE 5 Recovery of selected species with uncertainties in the recovery values: comparison of single and mutli-node balances. Stream numbers relate to the three node balance configuration. Species

Stream

With internal balance

Overall balance only

From

To

Recovery

SD

RSD (%)

Recovery

SD

RSD (%)

1

1

2

0.0206380

0.0003938

1.9083

0.0206322

0.0003979

1.9286

1

1

4

0.9771974

0.0003984

0.0408

0.9771817

0.0004038

0.0413

1

1

6

0.0021646

0.0000504

2.3263

0.0021861

0.0000534

2.4447

2

1

4

0.7845984

0.0021559

0.2748

0.7837257

0.0023094

0.2947

5

1

2

0.9698809

0.0002936

0.0303

0.9692268

0.0004581

0.0473

5

1

4

0.0003150

0.0000047

1.5057

0.0003150

0.0000048

1.5192

5

1

6

0.0298041

0.0002918

0.9790

0.0304582

0.0004570

1.5003

variance of an adjusted component flow to that of the corresponding measured component flow is simply the corresponding diagonal element of R. The sum of these ratios is the sum of the diagonal elements of R, which is known as the trace of the matrix. The average ratio is then the trace divided by the number of diagonal elements, that is, the size of the matrix R. R has a size N s ( N c + 1). The trace of the identity−1matrix is then N s ( N c + 1). The trace of the matrix G T (GΣG T ) GΣ is found by using the rule that to evaluate the trace of a matrix, it is permitted to rotate the order of multiplication of the matrices. One rotation provides:

134

Tr[G T (GΣG T ) GΣ ] = Tr[(GΣG T )-1 GΣG T ] -1

= dimension of [GΣG T ] = N n ( N c + 1) The result is: Tr{R} = N s ( N c + 1) − N n ( N c + 1)

(22)

and the average value is found by dividing by N s ( N c + 1), providing:  Var{adjusted component flow}  Nn Average   =1 − N flow} Var{measured component   s

Sunshine Coast, QLD, 9 - 12 May 2005

(23)

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= 29 per cent. Cast in these , the return on the investment in internal balances is rather good. The internal balances also provide additional insight into the process performance by allowing reliable calculation of recoveries for the three internal units. It also follows from the above result that if the internal process streams have measurement variances larger than two to three times the measurement variances associated with the boundary streams, there is a risk that the introduction of the internal flows will degrade the balance. However, this effect can be studied without actually having to calculate balances simply by using the expression in Equation 20, and the marginal cost of upgrading the quality of the internal stream data can be easily studied. It is important to note that in a flow sheet with a large number of identifiable feed streams, the real, single, feed to the process is the weighted average of all feeds.

Variance Reduction (lower accuracy)

1.00

Boundary Internal 0.75

0.50

0.25

0.00 0.00

0.25

0.50

0.75

1.00

Variance Reduction (higher accuracy) FIG 2 - Variance reduction ratios for balance with low accuracy internal stream measurements.

For the one node balance with four process streams, the value is 0.75 and for seven process streams with three balance nodes, the value is 0.57. The observed values for the calculations were ~0.7 and ~0.5, somewhat better than expected from the simplified theory. The difference of the result from the simplified theory is due to the fact that the covariance matrix is not diagonal. The above derivation demonstrates that the improved mass flow variances that arise when using more process streams is no accident. This result is entirely valid as long as there is no covariance between measured component flows and is approximately valid when there is covariance. The result here is remarkably simple and does not depend on the relative magnitudes of the variances or the detailed structure of the flow sheet. The unfortunate aspect of this result is that the ratio of nodes to streams cannot be increased much past 0.5. In fact, the flow sheet under study may have the maximal variance reduction. If the total variance of sampling, including sample preparation, is nominally equal to the analytical variance for all streams, duplication of all assay determinations will reduce the total variance by 25 per cent, yielding a variance improvement after single node balancing of 25 per cent. As long as the overall variance of the existing internal stream data is not more than double or treble that of the boundary streams, the introduction of internal balance nodes, as in this example, will reduce the variance of the adjusted component flows by 100 × (1 - 0.5/0.7)

Sampling and Blending Conference

CONCLUSIONS The simplified statistical analysis of the least-squares mass flow adjustment problem presented herein demonstrates that there is a substantial advantage to be had from the introduction of internal balance nodes when calculating a material balance around a processing operation. The introduction of internal balances to the common flow sheet under study here provides an average reduction in component flow variances of about 50 per cent in a practical situation. This reduction is achieved regardless of the measurement variances associated with the streams. The single node balance reduces component flow variances by only 30 per cent. While caution must be exercised when the relative variances of the measurements for the internal streams exceed two to three times those for the boundary streams, the introduction of internal balances provides a variance reduction benefit similar to the duplication of all boundary stream assays. On top of this benefit, for the common flow sheet under study, the internal balances also permit calculation of the recoveries for three unit operations where this was impossible with a single node balance. It is probable that the benefits proven for the simplified data covariance structure extend to somewhat more complex data structures. The methodology presented here permits the evaluation of flow sheets and data structures of quite general forms.

REFERENCES Hinckfuss, D A, 1976. The Bougainville Copper Limited concentrator, in Flotation, A M Gaudin Memorial Volume (ed: M C Fuerstenau), Vol 2, pp 1125-1144 (American Institute of Mining, Metallurgical and Petroleum Engineers Inc: New York).

Sunshine Coast, QLD, 9 - 12 May 2005

135

136

Sunshine Coast, QLD, 9 - 12 May 2005

Sampling and Blending Conference

APPENDIX F – DETERMINATION OF THE SAMPLING CONSTANT INDIVIDUAL PARTICLE ANALYSIS (50-PIECE EXPERIMENT) This experiment is based on the one detailed in “Sampling of Particulate Materials – Theory and Practice” by Pierre Gy (1979). It involves the collection of fifty or more of the largest pieces of material to be sampled and it measures their mass, volume and metal contents. The data is used in the following formulae to calculate the bias and variance associated with the sample. For an alternative version of this experiment as applied to gold ore sampling, see Bartlett and Hawkins. Fundamental Bias: m(FE) = -Y[1/MS – 1/ML] Fundamental Variance:

σ2(FE) = Z[1/MS – 1/ML] where:

MS = Mass of sample (g) ML = Mass of Lot (g)

When in the normal situation ML >> MS the equations can be written as: m(FE) = -Y/MS

σ2(FE) = Z/MS It can be shown that: Y = {(0,25v)/(aQMQ)}{Σq(aq – aQ) Mq2/Vq} Z = {(0,25v)/(aQ2MQ)}{Σq(aq – aQ)2Mq2/Vq} where: Mq = Mass of individual pieces (g) Vq = Volume of individual pieces (cm3) aq = Analytical value of individual piece (e.g. % Cu) MQ = Σq Mq v = (Σq Vq)/q q = Number of pieces aQ = Σq aqMq/MQ

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Using the data from the book by Pierre Gy (pp 276–278): %Fe

Mass Density Volume

aqMq

aq-aQ

W

X

55.310

163.0

3.88

42.00

9015.53 17.4000

11007.1571

191524.5343

21.330

121.0

2.75

44.00

2580.93 -16.5800

-5516.9950

91471.7771

37.860

116.0

2.97

39.00

4391.76

-0.0500

-17.2513

0.8626

45.620

127.0

3.26

39.00

5793.74

7.7100

3188.5792

24583.9459

13.910

78.0

2.69

29.00

1084.98 -24.0000

-5035.0345

120840.8276

50.860

102.0

3.92

26.00

5187.72 12.9500

5181.9923

67106.8004

42.650

60.0

2.61

23.00

2559.00

4.7400

741.9130

3516.6678

44.130

142.0

2.96

48.00

6266.46

6.2200

2612.9183

16252.3520

26.910

105.0

3.09

34.00

2825.55 -11.0000

-3566.9118

39236.0294

36.840

119.0

2.64

45.00

4383.96

-1.0700

-336.7171

360.2873

43.220

61.0

3.21

19.00

2636.42

5.3100

1039.9216

5521.9836

4.110

149.0

2.61

57.00

612.39

-33.8000 -13164.8035

444970.3586

6.040

95.0

2.57

37.00

573.80

-31.8700

-7773.6959

247747.6898

7.330

95.0

2.44

39.00

696.35

-30.5800

-7076.5256

216400.1541

43.910

198.0

2.57

77.00

8694.18

6.0000

3054.8571

18329.1429

53.140

107.0

3.96

27.00

5685.98 15.2300

6458.0841

98356.6204

61.580

172.0

4.00

43.00

10591.76 23.6700

16284.9600

385465.0032

62.720

120.0

4.14

29.00

7526.40 24.8100

12319.4483

305645.5117

38.200

119.0

3.61

33.00

4545.80

0.2900

124.4452

36.0891

44.480

104.0

3.71

28.00

4625.92

6.5700

2537.8971

16673.9842

44.930

121.0

3.67

33.00

5436.53

7.0200

3114.5400

21864.0708

47.560

91.0

2.84

32.00

4327.96

9.6500

2497.2391

24098.3570

63.520

148.0

3.70

40.00

9400.96 25.6100

14024.0360

359155.5620

7.300

52.0

2.60

20.00

379.60

-30.6100

-4138.4720

126678.6279

53.830

189.0

3.86

49.00

10173.87 15.9200

11605.6800

184762.4256

43.910

157.0

3.27

48.00

6893.87

6.0000

3081.1250

18486.7500

38.660

116.0

2.76

42.00

4484.56

0.7500

240.2857

180.2143

38.090

66.0

2.75

24.00

2513.94

0.1800

32.6700

5.8806

6.010

141.0

2.66

53.00

847.41

39.910

128.0

3.28

39.00

5108.48

21.360

138.0

2.76

50.00

22.800

89.0

2.87

31.00

-31.9000 -11966.1113 2.0000

381718.9511

840.2051

1680.4103

2947.68 -16.5500

-6303.5640

104323.9842

2029.20 -15.1100

-3860.8487

58337.4240

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%Fe

Mass Density Volume

aqMq

aq-aQ

W

X

45.730

150.0

3.57

42.00

6859.50

7.8200

4189.2857

32760.2143

4.790

100.0

2.44

41.00

479.00

-33.1200

-8078.0488

267544.9756

47.550

208.0

3.47

60.00

9890.40

9.6400

6951.0827

67008.4369

41.390

127.0

3.26

39.00

5256.53

3.4800

1439.2031

5008.4267

48.580

146.0

2.98

49.00

7092.68 10.6700

4641.6678

49526.5949

59.070

117.0

3.90

30.00

6911.19 21.1600

9655.3080

204306.3173

20.530

102.0

2.68

38.00

2094.06 -17.3800

-4758.4611

82702.0531

10.380

71.0

2.45

29.00

736.98

-27.5300

-4785.4734

131744.0840

44.820

81.0

2.79

29.00

3630.42

6.9100

1563.3279

10802.5960

3.400

82.0

2.28

36.00

278.80

-34.5100

-6445.7011

222441.1453

41.280

105.0

3.39

31.00

4334.40

3.3700

1198.5242

4039.0265

48.130

116.0

3.52

33.00

5583.08 10.2200

4167.2824

42589.6264

46.830

110.0

3.55

31.00

5151.30

8.9200

3481.6774

31056.5626

7.640

67.0

2.58

26.00

511.88

-30.2700

-5226.2319

158198.0403

where: W = (aq – aQ)*Mq2/Vq X = (aq – aQ)2Mq2/Vq by calculation: MQ = 5371 aQ = 37.91 v = 37.67 Y = 1.8 Z = 5.9 (Sampling Constant) using the relationship for Fundamental Variance, given previously:

σ2 = Z/MS the following minimum sample masses (kg) can be calculated at the indicated variances. Variance (Relative)

10%

5%

3%

1%

Mass

2.4

9.4

26.2

236

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APPENDIX G – ERROR AND VARIANCE PROPAGATION IN TOLL SMELTING OPERATIONS TREATING MULTIPLE CONCENTRATE STOCKPILES Chris J. Cutler, Jacques J. Eksteen* JSAIMM, Vol.106, pp 222–227. Re-published with permission of: The Southern African Institute of Mining and Metallurgy Department of Process Engineering, University of Stellenbosch, Private Bag X1, Matieland, 7602, South Africa *Corresponding Author, E-mail: [email protected] Abstract Metallurgical ing for toll treatment smelters pose a number of challenges. Multiple feed stockpiles have to be ed for as part of the monthly recovery estimation. Moreover, multiple metallurgical intermediates make up part of the inprocess inventory, which also influence the monthly recovery calculation. Errors propagate from the measurements of volumes, assays, moisture fractions and bulk densities in the various material types through multiple steps up to the point where the final recovery is calculated. These errors contribute non-linearly to the variance in the final recovery estimate. This paper develops the mathematical formulation for variance propagation in toll smelting operations, including the effect of in-process inventory, assuming measurement biases have been eliminated beforehand. Operational data from a Southern African copper smelter is used for a case study. The method of propagation of variance showed that uncertainties in stockpile assays were the main contributors to variance in the recovery estimate. Variance in the volume and bulk density uncertainties contributed a secondary, yet significant, proportion to the overall recovery variance. It was determined, for the given case study, that the recovery variance depended on the calculation method used and that variance propagation via the two-product formula was smaller than recovery variances calculated via the standard recovery formula. However, the probability that the two product formula will give inaccurate (versus imprecise) results is significantly more due to the practical difficulty of equiprobable sampling of tailings streams from smelter plants (which includes materials such as slags and flue dust). Recommendations are made on how to achieve a reduction in overall uncertainty for toll treatment smelters. Keywords:

Metallurgical ing, Copper Smelters, Pyrometallurgy, Variance Propagation, Errors, Precision, Sampling

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Introduction Improvements in metallurgical ing systems are often motivated by the drive to improve corporate governance and transparency. Many non-ferrous smelter managers are confronted with widely varying recovery estimates, depending on the calculation method used and the range of possible bases from which recovery is estimated. This has lead to recovery being a poorly defined, but highly significant number (“Key Process Indicator”) bandied about by metallurgists, managers and investors, all with a different perception as to what is really meant. In this paper it is shown how variance propagate through plant material balances. As stockpiles of raw materials, products and inventory always fall within the battery limits of a smelter plant they make up part of the working capital, a figure directly reflected on the balance sheet of each smelter. The cumulative effect of uncertainty in the measurement of stockpile volumes, moisture levels and bulk densities propagates non-linearly through to the variance in average plant feed assays and estimated recoveries. There is little published information on what precision can be ascribed to the copper recovery from concentrate to anode or blister over a given metallurgical ing period. Where the mine and the smelter are an integrated unit the overall recovery from mine to metal is of primary importance and changes can be made either at the mill or the smelter to maximize the overall recovery. Where the smelter recovery is extremely important is when concentrates are purchased or tolled because of the contract structures where the payable metal values mean that the smelter must achieve a certain minimum recovery in order to break even. This paper is an attempt to quantify the variance in the recovery from first principles using plant data where the ing principles are based on a “check in – check out” system (Wills, 2005) which is often used for pay-metal ing on a smelter. However, the “check in-check out” system becomes impractical at smelters for nonpay-elements and volatile elements such as sulphur (also elements such as arsenic, antimony, zinc) where bulk quantities are not transported via a checkpoint and continuous and variable losses occur via flue gases and fugitive emissions (such as from Pierce Smith converters, and molten matte transport in ladles). Various calculation strategies are now considered for a range of typical metal ing approaches. In the discussion below, bias (due to gross or systematic measurement error) has been ignored. In other words, all listed plant measurements are assumed to be unbiased. A number of methods exist for bias detection, such as the “cusums method” (Bartlett, 2005) for a lumped, plant-wide test, or through statistical tests (François-Bongarçon, 2005) on individual sampling points using paired t-tests. Should biases be found to be present, they should be eliminated through proper calibration, maintenance, or repair (such as leaks) as required. Sometimes total redesign of the sampling system is required according to the principles of representative sampling.

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Recovery Calculation from Assays only: Application of The Two Product Formula If the weights of monthly smelter feed, blister copper (product) and discard slag (tails) are F , P and T respectively and their corresponding assays f ,p, and t then :

F =P+ T (1) i.e. : total material input = total material output, over the period of a month, and

F ⋅ f = P⋅p + T ⋅ t (2) i.e. : the valuable metal, in this case copper, is balanced. The bar above the feed and tailings variables indicate representative quantity over the monthly period, with inclusion of stoiles for all feed or tailings material types. It follows that:

F ⋅ f = P⋅p + ( F – P) ⋅ t (3) which gives the split fraction to the product stream as:

P (f − t) = F (p − t ) (4) Subsequently, the metal recovery (Cu in this case) can be defined as :

R=

p⋅P ⋅ 100% f ⋅F or: (5)

R=

p⋅( f − t) ⋅ 100% f ⋅ (p − t ) (6)

The variance in any calculated amount (say recovery, or calculated stockpile mass, or average concentration) can be found from the variances in the measured quantities (such as assays, volumes, measured weights, etc.) through the summation of the squared partial derivatives of the calculated variable with respect to the independent measured quantities each multiplied with the respective variance in the measured quantity. The mathematical proof can be found in many a good text on mathematical statistics, such as Kreyszig (1970) and is based on the Taylor series expansion of the error of a multivariable function. This relationship is usually referred to as the “Propagation of Variance” or the “Law of Propagation of Error (LPE)”. This AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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paper applies this principle to smelter plants treating multiple material types, where stockpiles and in-process inventories are present. Xiao and Vien (2003) has noted that the addition of the second Taylor series term may be required in cases of large relative standard deviations of measurements. They state that the law of propagation of error (variance) is only an approximation, because second- and higher-order derivatives are neglected. For the non-linear variance propagation case, the precision of the approximation increases with decreasing relative variance, i.e. for small relative errors. Therefore, the truncation of the Taylor series approximation after the first term becomes less accurate when double digit relative standard deviations are measured on the plant, such as often found for slags and dusts (Eksteen et al., 2004). Additionally, it is assumed that the measurements are independent, which may not be the case in practice. The variance of a calculated variable (such as recovery) is a summation of the first derivative squared multiplied by the respective variances if the measurement variables are independent. For correlated measured variables, the covariances should be included in the error propagation estimate of recovery variance. Xiao and Vien (2003) did not consider the effect of stockpiles, but did show that Monte Carlo based methods estimated recovery variances to be of the same order but slightly larger than the variance propagation methods. Moreover, the Monte Carlo based methods allows one to see the effects of multiple normal and / or non-normal measurement distributions on the recovery distribution. As long as knowledge of the shape of the distribution is well defined, the Monte Carlo method is superior to variance-propagation. However, this knowledge is often not available, or can only be obtained at great cost, and plant metallurgists are often not inclined to perform Monte Carlo simulations on their desktop computer when a simpler formula-based estimation of variance is available. The variance in plant recovery, R, based on the derived two product formula (Equation 6), can be found from it’s derivatives as follows: 2

2

2

⎛ ∂R ⎞ ⎛ ∂R ⎞ ⎛ ∂R ⎞ V R = ⎜⎜ ⎟⎟ V f + ⎜⎜ ⎟⎟ V p + ⎜ ⎟ Vt ⎝ ∂t ⎠ p , f ⎝ ∂p ⎠ f ,t ⎝ ∂f ⎠ p ,t (7) where VR , V f , Vt and Vp are the variances in R, f , t and p respectively (for the month). For the two product formula, the derived propagated variance in recovery in term of assays and assay variances only, is:

⎡ p2 ⋅ t2 ( f − t )2 ⋅ t 2 V + ( p − f )2 ⋅ p 2 V ⎤⎥ ⋅ 1002 V R = ⎢ 2 Vf + ( p − t )2 p ( p − t )2 t ⎥⎦ f 2 ( p − t )2 ⎢⎣ f (8)

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Recovery Calculation from Masses and Assays: Traditional Recovery Estimate (Excluding Inventory) As copper recovery is defined by equation 5: R =

p⋅P ⋅ 100% where P, p, F , and f ⋅F

f are defined as in Equations 1 and 2. Then, as R = f(F, P, f, p), the variance of R can be found from the partial derivatives with respect to each variable: 2

2

2

2

⎛ ∂R ⎞ ⎛ ∂R ⎞ ⎛ ∂R ⎞ ⎛ ∂R ⎞ V R = ⎜⎜ ⎟⎟ Vf + ⎜ VF + ⎜⎜ ⎟⎟ Vp + ⎜ ⎟ VP ⎟ ⎝ ∂F ⎠ f , p ,P ⎝ ∂P ⎠ p , f ,F ⎝ ∂f ⎠ F , p ,P ⎝ ∂p ⎠ f ,F ,P (9) where VR , V f , VF , VP and Vp are the variances of R , F , f , P and p respectively. If

the derivatives are evaluated equation 9 becomes: 2 ⎡⎛ − p ⋅ P ⎞ 2 ⎛ − p⋅P ⎞ ⎛ P V R = ⎢⎜⎜ ⎟ V f + ⎜⎜ 2 ⎟⎟ VF + ⎜⎜ 2 ⎟ F⋅f ⎠ ⎝F ⋅f ⎠ ⎝F⋅ f ⎣⎢⎝

2

⎞ ⎛ p ⎟⎟ V p + ⎜⎜ ⎠ ⎝F⋅ f

2 ⎞ ⎤ ⎟⎟ VP ⎥ ⋅ 1002 ⎠ ⎦⎥

(10)

Recovery Calculation from Masses, Assays and Inventory Change When inventory changes are to be incorporated, over and above new metal bearing material, the copper recovery is defined as follows:

R=

p⋅P f ⋅F +

∑ ∆m a

i i

i

(11) where mi and ai represents the change in copper of inventory item i based on opening (o) and closing (c) masses and assays respectively, and the bar superscripts referring to actual monthly feed assay and net weight (all material types) fed to the smelter. Note that the there are different inventory material types to feed material types. The net change in in-process inventory, per material type i, between month end and beginning is:

∆mi ai = (mi ai ) − (mi ai ) o

c

(12)

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The associated variance in copper mass in inventory stockpiles over a month is:

⎡ ∂ (∆mi ai )⎤ ⎡ ∂ (∆mi ai )⎤ ⎡ ∂ (∆mi ai )⎤ ⎡ ∂ (∆mi ai )⎤ V ∆miai= ⎢ Vao + ⎢ Va c + ⎢ Vmo + ⎢ Vmc ⎥ ⎥ ⎥ ⎥ o c o c i i i i ⎣ ∂ai ⎦ mio ,mic ,aic ⎣ ∂ai ⎦ mio ,mic ,aio ⎣ ∂mi ⎦ aio ,mic ,aic ⎣ ∂mi ⎦ mio ,aio ,aic 2

2

2

2

(13) The sum of the variances for the total inventory change (all material types) is then the sum of the variances for the individual items (as they are statistically independent), i.e.:

V∆maT =

∑V

∆mi ai

i

(14) Estimation of Variances in Feed Masses and Assays each per Material Type The calculation for each material treated in the smelter is in two parts:

•

calculation of opening and closing stock variance

•

calculation of receipts variance

The net change in mass of ore material/concentrate type k for a one-month period is:

∆Fk = M dkr + M ko − M kc (15) The variance in feed mass is simply the sum of the opening and closing stockpile variances and the receipts variance, as the variances in opening, closing, and receipt masses are statistically independent (However, independence is questionable if there is a bias in stock measurement):

V∆Fk = VM r + VM o + VM c dk

k

k

(16) where the superscripts o , c and r refer to the opening stocks, closing stocks and receipts respectively.

Opening and Closing Stock Variance per Material Type The mass of a given stockpile (opening or closing) is defined by the following equation:

M k = Volk BDk (1 − mf k ) (17)

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where Mk , Volk and BDk are the dry weight, volume and bulk density stockpile of material type k, and mfk is the moisture fraction of material k (% moisture divided by 100). The variance of the dry weight of stockpile Mk is defined by the following equation :

VM k

⎛ ∂M k = ⎜⎜ ⎝ ∂Volk

2

⎞ ⎛ ∂M k ⎟⎟ VVolk + ⎜⎜ ⎠ BDk ,mf k ⎝ ∂BDk

2

⎞ ⎛ ∂M k ⎟⎟ VBDk + ⎜⎜ ⎠Volk ,mf k ⎝ ∂mf k

2

⎞ ⎟⎟ Vmf i ⎠ BDk ,Volk

(18) Then from the partial differentials:

VM k = [BDk (1 − mf k )] VVolk + [Volk (1 − mf k )] VBDk + (Volk BDk ) Vmf k 2

2

2

(19) Receipts Variance per Material Type The dry weight of a shipment of material k is represented by the Eq. 18 :

M dkr = (1 − mf k ) ⋅ M wkr (20) where Mdkr is the dry weight of the shipment, Mwkr is the as-received (wet weight) as measured on a weigh bridge and mfk is the moisture fraction as previously defined. The corresponding variance of the dry weight of the receipt is defined by the Eq.19:

VM r

dk

⎛ ∂M dkr = ⎜⎜ r ⎝ ∂M wk

2

⎞ ⎛ ∂M dkr ⎟⎟ VM r + ⎜⎜ wk ⎠ mf k ⎝ ∂mf k

2

⎞ ⎟⎟ Vmf k ⎠ M wkr

(21) Then from the partial differentials: r ) ⋅Vmf i VM r = (1 − mf k ) ⋅ VM r + (M wk 2

2

dk

wk

(22) Feed Assay Variance in Overall Feed (All Material Types) Average assay and associated variances for one material type over month duration The check in – check out system of ing defines the average feed assay fk for one material type as follows:

akr M dkr + ako M ko − akc M kc nett mass of copper fed of material type k during a month fk = = M dkr + M ko − M kc total mass of material type k during a month (23)

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where M dkr , M ko and M kc are the dry tonnages for receipts, opening and closing stocks respectively and a kr , a ko and a kc are the receipt assays, opening assays and closing assays for copper respectively. This gives the average copper assay, per ore or concentrate type, over the duration of one month, taking into receipt and the open and closing stockpiles. The variance of the average assay is then: 2

2

2

⎛ ∂f ⎞ ⎛ ∂f ⎞ ⎛ ∂f ⎞ ⎛ ∂f V f k = ⎜⎜ kr ⎟⎟ Va r + ⎜⎜ ko ⎟⎟ Va o + ⎜⎜ kc ⎟⎟ Va c + ⎜⎜ kr k k k ⎝ ∂a k ⎠ ⎝ ∂a k ⎠ ⎝ ∂a k ⎠ ⎝ ∂M dk

2

2

2

⎞ ⎛ ∂f ⎞ ⎛ ∂f ⎞ ⎟⎟ VM r + ⎜⎜ k o ⎟⎟ VM o + ⎜⎜ k c ⎟⎟ VM c dk k k ⎠ ⎝ ∂M k ⎠ ⎝ ∂M k ⎠

(24) where VM r , VM o and VM c are the variances of M dkr , M ko and M kc respectively dk

k

k

and Va r , Va o , Va c are the variances of a kr , a ko and a kc respectively. k

k

k

The partial differentials with respect to mass and assay are illustrated by the following:

⎛ ∂f k ⎞ M dkr ⎜⎜ r ⎟⎟ = r o c ⎝ ∂ak ⎠ M dk + M k − M k (25) and similarly through partial derivation with respect to a ko and a kc .

⎛ ∂f k ⎜⎜ r ⎝ ∂M dk

⎞ akr ⋅ (M ko − M kc ) − (ako M ko − akc M kc ) ⎟⎟ = (M dkr + M ko − M kc )2 ⎠ (26)

and similarly through partial derivation with respect to M ko and M kc . Average assay for a blend multiple materials A copper smelter usually treats more than one concentrate type and in some cases a variety of mattes and purchased scrap as well. The monthly average assay, as fed to the furnace (i.e. the equivalent of all stockpiles combined and fed via belt conveyor into the furnace over a one-month period) can be defined as follows:

∑ f ∆F f = ∑ ∆F k

k

k

k

k

(27)

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The total mass fed from new metal bearing material for the month (total mass excluding inventory change) is:

F=

∑ ∆F

k

k

(28) The variance of the average feed copper is defined as follows:

Vf =

∑ k

⎛ ∂f ⎜⎜ ⎝ ∂f k

2

⎞ ⎟⎟ V f k + ⎠

∑ k

⎛ ∂f ⎜⎜ ⎝ ∂∆Fk

2

⎞ ⎟⎟ V∆Fk ⎠

(29) The partial differentials with respect to mass and assay are illustrated by the following:

⎛ ∂f ⎜⎜ ⎝ ∂f k

⎞ ⎟⎟ = ⎠

∆Fk

∑ ∆F

k

k

(30) n

⎛ ∂f ⎜⎜ ⎝ ∂∆Fk

⎞ ⎟⎟ = ⎠

fk

n

∑ ∆F − ∑ f ∆F k

k =1

k

k

k =1

⎛ ⎜ ⎜ ⎝

n

∑ k =1

⎞ ∆Fk ⎟ ⎟ ⎠

2

(31) For a total of k=1..n material types (concentrates fed to smelter). So that the variance in the average feed assay, including the effect of stockpiles is given by:

Vf =

⎛ ⎜ ∆F k ⎜ ⎜ ∆Fk ⎜ ⎝ k

∑∑ k

2

⎞ ⎟ ⎟ Vf + ⎟ k ⎟ ⎠

∑ k

⎛ ⎜ ⎜ fk ⎜ ⎜ ⎜ ⎜ ⎝

2

⎞ ⎟ f k ∆Fk ⎟ ∆Fk − k =1 k =1 ⎟ V∆F 2 k n ⎟ ⎛ ⎞ ⎜ ⎟ ∆Fk ⎟ ⎜ ⎟ ⎟ ⎝ k =1 ⎠ ⎠ n

n

∑

∑

∑

(32) Product Weight Variance In the case considered here the product blister is weighed on an assized weighbridge with an accuracy of ±20kg in 100 tones. Any weight measurement on this bridge has a relative standard deviation (RSD) is 0.01. Assuming a normal distribution of measurement uncertainties, the standard error can be said to be ±1.96×σ, within a 95% confidence interval (Kreyszig, 1970). AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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NS

∑ (m − m )

2

Where: σ =

j =1

n −1

(33) and NS is the number of weight determinations during calibration.

Product Assay Variance The RSD was calculated by analysis of assay results from multiple spoon samples taken from the same ladle during blister copper pouring. The RSD was determined as 0.047. Discard Slag Assay Variance Although the RSD for copper in discard slag was not directly measured, work done by Eksteen et al. (2004) as well as within the AMIRA P754 project suggests that the RSD is likely to be in the range 15 - 20% for metal/matte elements, where the element/species can be entrained (e.g. Cu in this case). The RSD is much smaller for dissolved metal losses than for entrained metal losses in slags. Due to the difficulty of achieving equiprobable sampling for melts, there is a significant risk of introducing bias due to the sampling process. This bias is also difficult to assess as finding the “true” value is not economically viable. There is therefore a very good reason for using feed and product assays only, rather than including waste materials (such as slags and dusts). However, the inclusion of waste does aid in material balancing (using data reconciliation), but only if the composition can be determined without significant bias. Worked Example The base data for the following cases is July 2005 actual data for a Southern African copper smelter A. For confidentiality reasons the sources of the materials treated are not given. Recovery and Variance Calculation from Assays using the 2-product formula: Actual Input Data

f : average copper assay in feed mix

: 32.95%

p : copper assay in blister copper

: 98.85%

t : copper assay in discard slag

: 1.30%

V f : variance in average % copper in feed mix

: 0.58

Vp : variance in % copper in blister copper

: 0.0021

Vt : variance in % copper in discard slag

: 0.0676

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Then by substitution in Eq. 6: R = 97.32% and using Eq. 7: VR = 147.18 Vf + 1.65 Vp + 43572.40 Vt which on substitution with the values given above gives: VR = 0.008 + 0.0000003 + 0.295 = 0.303 and therefore the σR = 0.55, and the standard error with 95% confidence limits = ±1.1% From this calculation the recovery at the 95% confidence level will be 97.3% ± 1.1%. In this case the recovery variance is dominated by the variance in copper in the discard slag with a small contribution coming from the variance in the average copper in feed mix. Recovery and Variance Calculation from Masses and Assays Actual Input Data

f : average copper assay in feed mix:

32.95%

p : copper assay in blister copper:

98.85%

t : copper assay in discard slag:

1.30%

V f : variance in average % copper in feed mix:

0.58

Vp : variance in % copper in blister copper:

0.0021

Vt : variance in % copper in discard slag:

0.0676

F : total dry tones of feed:

8103 mt

P : blister copper produced:

2703 mt

VF : variance in the weight of feed:

11771 mt2

VP : variance in the weight of blister copper:

0.0729 mt2

Then by substitution: R = 100.1% and VR = 0.00015 VF + 0.0014 VP + 92265.5 Vf + 10251.7 Vp VR = 1.80 + 0.0001 + 5.34 + 0.002 = 7.14 and therefore σR = 2.67 From this calculation the recovery at 95% confidence limits is: R = 100.1% ± 5.2% AMIRA P754: Code Guidelines Release 3 20070219 Commercial in Confidence

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In this case the variance in the recovery is significantly influenced by the feed assay variance and less so by the feed mass variance. The calculated recovery is not meaningful from a practical perspective, because it does not yet take inventory change into . Recovery Calculation from Masses, Assays and Inventory Change Input Data

f : average copper assay in feed mix:

32.95%

p : copper assay in blister copper:

98.85%

t : copper assay in discard slag:

1.30%

V f : variance in average % copper in feed mix:

0.58

Vp : variance in % copper in blister copper:

0.0021

Vt : variance in % copper in discard slag:

0.0676

F : dry tones of feed:

8103 mt

P : blister copper produced:

2703 mt

VF : variance in the weight of feed:

11771 mt2

VP : variance in the weight of blister copper:

0.0729 mt2

∑ ∆m a

80 mt

i

i

= copper in inventory change:

i

V∑ ∆miai : variance in copper in inventory change :

219 mt2

i

Then by substitution : R = 97.2% and VR = 0.0116 VF + 0.0013 VP + 286.3 V f + 9663.9 Vp + 0.0353 V∑ ∆mi ai i

VR = 1.6 + 0.00009 + 4.72 + 0.002 + 0.27 = 6.59 and σR = 2.6 For this case the inventory variance has some significance but the feed assay variance is still dominant. Discussion of Results This work was undertaken to try and understand why the recovery numbers and uned losses in smelter A varied by up to 5% in any month ing period when the process streams and recycles were ed for and the flowsheet was closed except for discard slag.

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While the calculation of recovery variance from assays (two-product formula) gives good numerical results, it is certainly the least accurate because of the difficulties in practice of estimating the tailings assay variance which in this case is discard slag. Moreover, due to the difficulty of implementing equiprobable sampling practice for melts, there is a significant probability of introducing bias through systematic error due to poor sampling. The calculation is relatively insensitive to feed assay variance mostly due to the totally overpowering effect of tailings variance. It is preferable to go back to the calculations based on mass and assays including change in inventory which are inherently more accurate, because reasonable estimates can be put around the basic parameters. The recovery calculation in this case does not rely on any variances associated with weights or assays of discard slag where the sampling technique is paramount and furnace conditions need to be stable at all times for the discard slag assay to mean anything. In the case of the mass estimates the variances in stock pile volumes, bulk densities and moisture levels can be established in the field. In the case of assays the sampling and analytical errors are reasonably well known. The major contributor to the feed assay variance is the assay variability between lots and within lots which comes back to the performance of the concentrator at the mine. The initial assumption in this analysis was that the variance in copper assay between lots of nominally 400 tones would be representative of the variance within a lot. This was checked using 10 sequential lots of the individual concentrates and 10 wagons within a lot. The results are shown in Table 1.

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Table 1 - Concentrate Assays and measured? Standard Deviations Material Type

Lot Average Cu %

σ

Wagon Average Cu %

σ

Concentrate A

23.70

0.92

23.97

1.48

Concentrate B

51.65

3.1

Concentrate C

29.39

1.1

Concentrate D

21.76

2.7

20.50

3.1

Concentrate E

30.84

2.7

31.62

3.0

Table 2 is a summary of sensitivities against the base case as calculated using masses, assays and inventory change. Table 2 – Contributions to variance: Comparison Against Base Case

Comp

Base Case

% Cont

Case 1

% Cont

Case 2

% Cont

Case 3

% Cont

Case 4

%

VF

1.60

24

1.60

42

0.04

1

0.04

2

2.43

29

VP

0.00009

0

0.00009

0

0.00009

0

0.00009

0

0.00009

0

Vf

4.72

72

1.90

50

4.32

93

1.80

85

5.63

68

Vp

0.002

0

0.002

0

0.002

0

0.002

0

0.002

0

0.27

4

0.27

7

0.27

6

0.27

13

0.27

3

V∑ ∆mi ai i

VR

6.6

3.8

4.6

2.1

8.3

σR

2.6

1.9

2.2

1.5

2.9

The 5 cases in Table 2 are as follows:

•

The Base Case is the actual case as observed for a given month for smelter A.

•

Case 1 assumes that the copper assay variance for concentrate A was applicable for all concentrates

•

Case 2 assumes that all concentrates received during the month were treated during the month

•

Case 3 is the “best” case where there are no opening and closing stocks and the assay variance for concentrate A applies to all concentrates

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•

Case 4 assumes that there are opening and closing stocks for all concentrates treated during the month

In all cases the average feed assay variance is the major contributor to the recovery variance although the mass variance becomes significant where there are opening and closing stocks for each concentrate type. As for all stockpiles, managing between empty piles is a practical way to control this problem. Piles which almost never empty are convenient – especially for hiding problems. In all cases the contributions of variance of both the product weight (blister copper) and the product assay were insignificant, due to the high degree of precision. Conclusions The following conclusions could be drawn from the analyses presented above.

•

A variance propagation model has been formulated for a copper toll treatment smelter. This variance propagation model took into changes in stockpiles, as well as changes in inventories.

•

It was determined that, despite the 2-product formula being more precise (i.e. smaller variance in recovery estimate), it is inherently less accurate, due to challenges associated with the sampling of melts, dusts and gases in cases when these become the “tailings”, or waste of a smelter.

•

The assay variance in feedstocks was always the largest contributor to variance in recovery, and was always greater than the contribution due to variances in net masses fed (which includes stockpile and inventory changes).

•

The large observed variances in recoveries could be explained through the variance propagation model.

•

The actual observed case (Base Case) showed that the standard deviation in the recovery estimate was 2.6%. The standard error in the monthly recovery estimate is therefore +/- 1.96 × 2.6% = ±5.1% with 95% confidence.

•

There is therefore a clear business case to improve sampling practice and instrumentation on this specific toll treatment smelter. However, the most effective way to reduce the recovery variance is to reduce the assay variability between stockpiles. Subsequent to the reduction in assay variability, is the reduction in the variability in stockpile masses due to variability in volumes, bulk densities and moisture fractions. Good control over blending might go a look way in achieving this goal, i.e. variation between material type assays can be reduced through proper blending based on known assays of all material types being blended. However, doing this well usually requires a major capital investment.

•

Bulk density variation can be reduced through the use of a narrow particle size distribution, volume variances can be reduced through improved surveying techniques and the variance in moisture fractions can be reduced by, for example, shielding the stockpiles from environmental changes in moisture (e.g. using a shed).

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•

As the variance in recovery is known, it serves as a baseline for developing improved material balance closure (data reconciliation) techniques, such as least squares (Narasimhan and Jordache, 2000; Romagnoli and Sánchez, 2000) or maximum likelihood methods (Kreyszig, 1970; Seinfeld and Lapidus, 1974). For these methods, all information is used for all streams, elements and nodes for which metallurgical ing information is available. Repeating these methods using a Monte Carlo simulation approach based on known measurement uncertainty distributions allows one to generate the distribution in recovery estimates and therefore to calculate the recovery variance after reconciliation. Other possibilities are more elements assayed and better feed mass measurement and sampling. In essence this would aid the metallurgist to investigate various options to determine where the most variance reduction can be obtained for the least investment.

•

It should be noted that no assumption of the distributions of measurement uncertainties were made. This does not influence analyses, except where standard errors are converted into standard deviations, or visa-versa, where an assumption of normality is made. The normality assumption influences the 95% confidence intervals. A skewed (non-symmetrical) distribution will lead to non-symmetrical confidence limits around the expected value.

•

The error propagation model is based on fundamental theorems in mathematics and statistics and material balances and is not constrained to apriori knowledge of the error distributions. However, practical implementation of the model requires a method to translate standard errors to standard deviations, and the quantification of the error distribution through fitting the histogram developed through well-planned sampling campaigns and / or the continuous use of well calibrated sampling and measurement devices. In short, we need a model of how measurement error itself behave at different measurement values.

•

Errors which lie outside the propagation of variance models presented above may be caused by bias or by data which is simply in error for some other reason such as mis-entry of data or mislabelling of samples.

•

Large relative standard deviations or measurement variances may require a more complex variance propagation formulation, as the first term of the Taylor series approximation becomes inadequate to quantify the full effect of measurement errors on the variance in the calculated variable (recovery in this case).

Acknowledgements This project was performed at the University of Stellenbosch as a subproject of the AMIRA P754 program on Metallurgical ing and Reconciliation, done in collaboration with the Julius Kruttschnitt Mineral Research Centre and the University of Cape Town . The AMIRA P754 program is aimed at delivering a Code, a set of Guidelines and a set of Toolboxes for Metallurgical ing and Reconciliation with the aims of:

•

improving transparency at the corporate governance level;

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•

quantifying the causes and sources of “uned losses” and bias;

•

quantifying the uncertainty in measurements of masses, assays, stocks and recoveries;

•

quantifying the investment risk associated uncertainties in working capital (inventories and stocks) and operating performance (recovery).

The authors would like to acknowledge the contributions of the AMIRA P754 sponsors and the contributions of the National Research Foundation through its THRIP (Technology and Human Resources for Industry Program) initiative.

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References Bartlett, H.E., Confidences in Metallurgical Balances Estimated from Errors in Mass Measurement, Sampling, and Analytical Determinations, Exploration, Mine, Met and Environmental Sampling 2005 (EMMES 2005), SAIMM, Johannesburg, 3-4 November, 2005 Eksteen J.J., Frank S.J., Reuter M.A., Distributed compositional and temperature nature of melts in submerged and open arc furnaces in high carbon ferrochrome production, Proceedings of the 10th International Ferroalloys Congress (INFACON X), 1-4 February, Cape Town, South Africa, pp. 83 -93, 2004 François-Bongarçon, D., The Philosophy of Statistical Bias Testing Revisited and Applied to Automatic Samplers, Proceedings of the 2nd World Conference on Sampling and Blending, 10-12 May, Sunshine Coast, Queensland, Australia, pp. 1517, 2005 Kreyszig, E., Introductory Mathematical Statistics: Principles and Methods, John Wiley and Sons, New York, N.Y.Y., pp. 355-357, 1970. Narasimhan, S., and Jordache, C. Data Reconciliation and Gross Error Detection: An Intelligent use of Process Data, Gulf Publishing, Houston, Texas, 406 pp. 2000 Romagnoli, J.A., and Sánchez, M.C., Data Processing and Reconciliation for Chemical Process Operations, Process Systems Engineering, Vol. 2, Academic Press, San Diego, California, 270 pp., 2000 Seinfeld, J.H., and Lapidus, L., Mathematical Methods in Chemical Engineering, Vol. 3 (Process Modeling, Estimation and Identification), Prentice-Hall, Englewood Cliffs, New Jersey, 545 pp, 1974. Wills, B.A., Mineral Processing Technology, 7th Edition, Butterworth-Heineman, 2005 Xiao, Z., Vien, A., Limitations of variance analysis using propagation of variance, Minerals Engineering, Vol. 16, pp. 455–462, 2003

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APPENDIX H – USEFUL WEB SITES ANSI – www.ansi.org AOAC – www.aoac.org APLAC – www.ianz.govt.nz/aplac ASME – www.store.asme.org ASTM – www.astm.org British Standards – www.bsonline.bsi-global.com CITAC – www.citac.org COMAR (Reference Material Data Base) – www.comar.bm.de DIN – www.normas.com/DIN/indexes/ EURACHEM – www.eurachem.org IEC – www.iec.org ILAC – www.ilac.org Institute of Measurement and Control – www.instmc.org.uk ISA – www.isa.org ISO – www.iso.ch (ISO) REMCO – www.iso.org/remco JIS – www.jis.org NATA – www.nata.asn.au NIST – www.nist.gov OIML – www.oiml.org SABS – www.stansa.co.za SANAS – www.sanas.co.za Standards Australia – www.standards.org.au UKAS – www.ukas.com VAM – www.vam.org.uk

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