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Novel techniques were developed to obtain samples from and make measurements in the shaft of the flash furnace at the Kalgoorlie Nickel Smelter. The data obtained was used to validate mathematical models of the burners and the reaction shaft. Particle, gas and wall temperatures were measured along with the oxygen potential of the molten particles. Collected particles were characterised by sizing, chemical analysis and optical microscopy. The region of the shaft immediately beneath the burner where the rate of change was most rapid received special attention.

Jan 1, 1993

I have chosen this subject in view of the fact that, while there are many and ample opportuuities of getting reliable assays made, there are but few who are in a position to obtain reliable data on the sampling of minerals and metals my long experience at Port Pirie, where, for five years, I had charge of the Refining and Sampling Mill, showed me how important it was that a correct sample should be taken.Weighing; This, to the ordinary layman, seems so simple that no expert knowledge is needed, but in actual practice there are hundreds of pounds to be lost by the lack of knowledge in this department.(1.) There is the matter of weighing in large or small quantities.(2.) The class of weighing machine used.(3.) The accuracy of weights used.We all know that, say, 1000 tons of ore cannot be weighed in one parcel; but the fewer the weighings the less loss to the seller (and usually wishes it weighed in small lots carefully) for it is a well known fact that the most sensitive scales take a certain weight to show distinctly the indication of the balance, or, in other words, what is known as "the pull of the scales." As for instance, 1000 tons weighed in lots of 1 c. each, taking the pull of the scale as being equal to 1 lb., would give a result of 9 tons loss to tbe seller, and as most of the ore sent to Customs works is worth 5. -£20 per ton-this would mean £180 loss to the careful man.

Jan 1, 1901

In spite of the efforts done in the past to implement in Codelco best practice guidelines and standards on several important sampling, assaying and variability issues, many people, particularly top managers and operators, are still not aware of the relevance of the use of the principles and practice of geostatistics, sampling theory, analytical chemistry and the understanding of processes variability from rock to cathodes. As a result, an important challenge for the sampling community is to find out the causes of this lack of awareness and to develop communication skills to influence the organisation, particularly the top management level. This situation is not affecting only Codelco; it is a common characteristic of the copper mining industry. The main cause is cultural. Judeo-Christian and occidental sciences are deterministic. This implies a disregard of process variation and the assumption that homogeneity is omnipresent. Another important aspect of the cultural challenge is education. Education is the most efficient way to transfer a system of ideas, traditions and practices. To implement proper courses of sampling theory and geostatistics and support to multidisciplinary education at universities, mining companies will improve teamwork between geologists, mining engineers, metallurgists and chemists. The improvements will imply a more sustainable and efficient mining business.

Mar 1, 2010

By P. Groleau, O. Tavchandjian, D. Marcotte

In open pit mining, each small mining unit (SMU) is selected for mill processing based on an estimate of its true grade. The estimation is usually done using analyses of cuttings from blast holes within that SMU. One common estimator used for selection is the arithmetic average of within-SMU samples. This estimator is conditionally biased. The effect of a conditional bias is to overestimate the profit really recovered from the deposit. For this reason, a conditionally unbiased estimator is preferable. Increasing the number of samples generally improves the accuracy of the estimation (and diminishes the conditional bias, if present, of most estimators). However, there exists a practical limit where an additional sample does not improve the estimation sufficiently to cover the expense of making the analysis. This limit is specific to each deposit and depends on geostatistical parameters, economic factors, and mining parameters. This study examines how the number of samples affects the recovered reserves and the resulting profit. Also, the effects on optimal profit of varying the different factors are examined. Globally, the number of samples required increases with the selectivity of the operation. For a given ore deposit, the greater the number of SMUs selected for processing, the fewer samples are required and vice-versa.

Jan 1, 1998

By Harrison Cooper, Stephen Pack

"Data from samples taken during rotary air blast and reverse circulation drilling are crucial to properly assessing economics of mineral deposits in the case of exploratory work, and are equally critical in accurately delineating ore and waste in execution of mine operation plans. Past studies of sampling procedures, comparing results of conventional splitting to more precise extraction of representative samples, illustrate the significant degree of error frequently encountered. Economic impact of error in mine blast hole sampling work can be shown to result in theoretical losses valued at millions of dollars annually in operating large scale mining projects. Results of past drill sampling studies are reviewed.Drill sampling technique can be improved by collecting coarse and fine particles together and extracting samples for assay from the complete flow of rock and dust simultaneously. This can be accomplished with a collection unit attached to the drill stem through which all except a small proportion of extreme fines are retained. The bulk mass can be static sampled as flow takes place through the collector when size reduction is not required. Alternately, collected mass with fines and coarse intermingled can be processed for increased accuracy through size reduction to obtain proportional samples."

Jan 1, 1999

By S Brochot

During the development of a mining project, samples are taken for metallurgical tests. The sample can be a composite sample made of material coming from a selection of various drilling products or a sample coming from a mining test. This sample can represent several tonnes of material which are analysed and split to be used in various tests.In the current case, a composite sample is dedicated to semi-autogenous grinding (SAG) pilot plant tests. The property of interest is then the size distribution of the material. The sample is mixed and a subsample is taken to measure the size distribution which is considered as the feed size distribution. Then the remaining material of the sample is divided in several subsamples for batch grinding tests at different operating conditions. The product of each grinding test is then analysed to measure its size distribution.The grinding performances are analysed at the light of the measured size distributions for feed and product. But what is the accuracy of the measurement of these size distributions? What is the variability of the feed knowing each feed is just a subsample of the initial sample?The Pierre Gy theory of sampling answers these questions.The current paper presents, through a real example, the calculation of the measurement error of the size distributions and the variability of the feed between the different tests. The uncertainties of the grinding performances are then calculated and their impact on the results interpretation is then analysed.CITATION:Brochot, S, 2012. Sampling for grinding pilot plant test - the effect of size distribution variability, in Proceedings Sampling 2012 , pp 127-134 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 21, 2012

"During the development of a mining project, samples are taken for metallurgical tests. The sample can be a composite sample made of material from a selection of various drilling products or a sample from a mining test. It can be also a sample from an existing plant in order to investigate process enhancement. This sample can represent several tonnes of material which are analysed and split for use in various tests.In the current case, a composite sample of gold ore is dedicated to a Gravity Recoverable Gold (GRG) test. The properties of interest are the head grade of gold, the size distribution of the material, and the gold assay per size class. The sample is mixed and a sub-sample is taken to measure the feed properties: the size distribution and the gold assay per size class. The remaining material of the sample is subject to the first stage of concentration using the laboratory Knelson concentrator. The concentrate is analysed in its entirety, while a sample of tailings is taken for the same analysis. The remaining part of tailings is subject to grinding to feed the second and third stages of concentration. Similar sampling is performed at each stage.The concentration performances are analysed in light of these measurements. But what is the accuracy of the measurements of the properties? Pierre Gy’s theory of sampling, associated with a heterogeneity model built using the GRG test results, will answer this question. The current paper presents, through a real example, the calculation of the errors of the measurements of size distributions and gold assays performed during the GRG test. The uncertainties of the concentration performances and liberation size estimate are then calculated and their impact on the results interpretation is analysed.INTRODUCTIONDuring the development of a mining project, samples are taken for metallurgical tests. The sample can be a composite sample made of material from a selection of various drilling products or a sample from a mining test. It can be also a sample from an existing plant in order to investigate process enhancement. This sample can represent several tonnes of material which are analysed and split for use in various tests. In order to take into account the ore variability in terms of gold deportment, it is recommended to perform variability studies (Dominy, Murphy, & Gray, 2011, Dominy, 2013) using samples representing various horizons or years of production. But for a given ore type, it can be necessary to perform many tests with different operating conditions (Brochot, 2012a). In that case, the variability of the samples is not due to the heterogeneity of distribution of the deposit, but only on the heterogeneity of constitution of the primary sample. The level of confidence associated with the estimated performance parameters is directly linked to the overall error (including sampling error) of the measurements performed during the tests."

Jan 1, 2016

The Pilbara iron ore industry has seen rapid growth over the last five years and is now bracing itself for another expansion which will take its seaborne trade to beyond 500 Mt/a. To effectively utilise port capacities, port stockpile turnover will become extremely rapid and each stockpile built will have to be of a quality which is precise and unbiased.Sampling systems at the older Pilbara mine sites were generally designed during the 1980s and reflect the practices which were considered adequate at the time. Hence, the precision of measurement at production sites is generally unknown and quality assurance (QA) of sampling facilities falls short of what current best practice suggests. This paper suggests practical improvements in sampling practices and quality assurance, which will assist the industry in achieving quality objectives in times of rapid mining expansion.

Jan 1, 2008

By M Fritz, B Ziegelaar

"For moisture samples, the International Standard for iron ore sampling states the following: Moisture samples shall be processed as soon as possible and test portions weighed immediately. If this is not possible, samples shall be stored in non-absorbent airtight containers with a minimum of free air space to minimise any change in moisture content, but should be prepared without delay. ISO 3082 (2009)Prescribing what to do is of course easy. Putting it in practice is, however, a little more problematic.The iron ore industry has seen quite some changes in the last ten to 15 years, such as: a large increase in the volume of seaborne trade new ore types and blends coming onto the market new mines, processing plants and port facilities producing more and loading faster an increase in wet processed ore products a dramatic rise and then equally dramatic fall in the iron ore price followed by a recent recovery.All of the above have had an effect on sampling facilities. The large increase in volume coupled with ambitions such as labour efficiency, safer operations and higher precision has driven the installation and retrofitting of automated sampling and robotic sampling preparation plants in order to keep up with increased quality control.Designers of sampling facilities are invariably instructed to build plants that comply with ISO 3082. In general this means that the plants are compliant in respect of the number of primary increments, gross sample masses and sample division rules. The clause at the beginning of this abstract is mostly overlooked as it only appears in a section (4.1) describing the basic requirements of a sampling system.An error of 0.1 per cent in moisture changes the value of 1 Mt of iron ore at US$50/t by US$50 000. At 700 Mt/a of Australian iron ore exports, this is US$35 M. Frequently, errors from 0.5 to 1 per cent in moisture measurements are encountered.In comparison an error of 0.1 per cent Fe is US$0.081 (US$50/t and 62 per cent Fe base) per tonne, that is US$81 000 per Mt or about US$51 M for the same Australian annual iron ore exports.It is somewhat mystifying that we in the ISO Technical committees spend extraordinary energy (= money and time) on improving measurement techniques, whilst the big costs are in sampling errors. This is particularly true for moisture measurement. Our paper quantifies some of the sources of moisture measurements errors and suggests mitigating actions.CITATION:Ziegelaar, B and Fritz, M, 2017. Sampling for the percentage moisture in the iron ore industry, in Proceedings Eighth World Conference on Sampling and Blending, pp 271–274 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

May 9, 2017

By I T. Ross

Sampling drawpoints in block cave mines has been a contentious issue for many years. Doubts have been expressed as to the validity of the process used at most mines. This paper outlines the sampling practice commonly in use, discusses the resulting issues associated with reconciliation and drawpoint closure decisions. The paper also looks to the future and highlights issues for some block cave mining operations currently planned to have in the order of one thousand active drawpoints. It is likely that these operations will require a new approach to drawpoint sampling. There are several potential technologies currently available although some appear to be better suited than others to replace the current approaches. CITATION:Ross, I T, 2012. Sampling in block cave mines, in Proceedings Sampling 2012 , pp 81-86 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 21, 2012

By R. J. Mitchell

Results from investigations into impurity distributions over cathodes are presented with implications for various sampling methods, Selection of an optimum treatment procedure for the test sample is outlined. The impact of both these studies on the development of an in-house method for sampling Isa Process cathode is discussed. The economic compromise actually undertaken, satifying wants and needs to sustain confidence in high grade cathode purity is detailed.

Jan 1, 1987

By Jan Visman, S. J. Aresco

INTRODUCTION The accurate sampling of coal, as with most minerals, is a difficult task. Coal is a heterogeneous material made up of different types of coal and varying amounts of mineral matter. The product as mined may contain all the layers of coal and impurities found in the seam as well as portions of the stratas above and below the coal seam. The preparation plant is the device to size, crush, and/or remove impurities so the coal may be shipped as a salable product. Sampling may be needed at many stages in the preparation process. Although this chapter is primarily concerned with sampling in connection with preparation processes, the principles apply to all coal sampling. The main reasons for sampling in connection with coal preparation are: 1) To determine the washability characteristics of the raw coal by means of float and sink tests. These may be used for the design of a preparation plant, prediction of results to be obtained, or monitoring raw coal delivered to the plant. 2) To check the performance of various operating units in the plant, either by float. and sink tests or chemical analyses. 3) To determine the analytical characteristics of the products produced, such as moisture, ash, Btu, sulfur, etc. HISTORY The earliest accepted methods of sampling coal in the United States were given in US Bureau of Mines (USBM) publications by J. A. Holmes,l G. S. Pope,2 and A. C. Fieldner.3 The American Society for Testing and Materials (ASTM) more or less adopted the methods proposed by USBM in their Standard D-21-1916 and it remained a standard for many years. Briefly, this standard called for a 500 lb (227 kg) sample for coals less than 94 in. (19 mm) in size and a 1000 lb (454 kg) sample for all coals over 94 in. (19 mm) in size. No mention was made of increments, but there was an instruction that the sample should spread over the coal

Jan 1, 1979

By Francis F. Pitard

The presence of elements, minerals, or chemical compounds in materials at very low concentrations is becoming a constantly increasing concern for several segments of the Industry. Some trace elements or chemical compounds may be considered as hazardous, some are simply considered as undesirable In complex processes where they could be constantly recycled and end up as the major factor of decreasing quality for some valuable products. In some other cases, the presence of a trace element or mineral may be highly desirable because of its economical value: precious metals are falling into this category, they probably were the first trace elements to draw our attention. Tremendous quantity of money is spent for analysis at the trace level, also the analytical "tool" has been considerably improved during recent years, but still the sampling aspect of the problem remains obscur for many, and because of it, large quantity of analytical efforts become meaningless. Not only the absence of sampling strategies is badly affecting the representativeness of a large majority of samples, but furthermore for the same reasons, many laboratories are failing In their endeavor to prepare reliable standard reference materials. The reason of this failure has its roots in the unability to estimate effectively the sampling characteristics of the element, mineral, or compound of interest, not to mention the unability to perform precise and accurate complete analysis, so essential for the instrumental analyst (1). In this paper a strategy to perform a better sampling operation is recommended. SELECTION OF A PROBABILISTIC APPROACH AS SOON AS PRACTICALLY POSSIBLE It Is essential to differentiate probabilistic sampling from non-probabilistic sampling. As Illustrated in Figure 1, the sampling operation Is a selection process which could be performed In two different ways: I. A probabilistic selection process according to which all elements of a lot of material to be sampled are submitted to the selection with a same probability of being selected. 2. A non-probabilistic selection process according to which all elements of a lot of material to be sampled are submitted to the selection with different probabilities of being selected.

Jan 1, 1986

By Francis F. Pitard

North America had an Important role in the early development of sampling theories. Unfortunately many progressive ideas were soon frozen into a series of guidelines based on the philosophy that "honest sampling only requires good judgment and practical experience". Such a philosophy appeared to be good enough In the early age of the mining Industry; however, it became quickly necessary to find new approaches. During the last thirty years, Pierre Gy's sampling theory provided tremendous breakthrough in the comprehension of this new science, nevertheless went unnoticed in North America. Sampling always generates complex problems and as the philosopher and mathematician Descartes suggested: "A complex problem can often be dissociated Into a series of several simpler problems, only then you try to solve those one by one". The only way to solve sampling problems is to consider each component of the overall sampling error: components of a continuous model such as long-range quality fluctuations, periodic quality fluctuations, weighting fluctuations; components of a discrete model such as materialization errors, delimitations errors, extraction errors, fundamental errors, grouping errors, and segregation errors. PAST AND CURRENT PRACTICES Many sampling methodologies have been developed, improved, and standardized during the first half of this century. Accordingly, sampling equipment has been proposed, selected, and used by standards committees. Today, there is a tremendous variety of sampling methodologies and corresponding tools offered to anyone with limited experience in sampling and it is very easy for a chemist, metallurgist, geologist or miner to spend considerable money in equipment transgressing the most basic rules of sampling correctness. Sampling stations that never worked properly in modern plants often cause engineers to abandon them and return to a primitive form of grab sampling.

Jan 1, 1986

By J. L. Fuller

Thousands of mine air samples are taken underground each year by MSHA inspection personnel in order to further assure that miners have a safe and healthy environment in which to work. While most air samples are taken with stain tubes, many samples are obtained in evacuated containers and returned to a laboratory for more accurate analysis on a gas chromatograph. In order to enhance the accuracy, reliability and repeatability of the samples taken with evacuated containers, improved procedures have been developed, which are described in this report, for filling two different types of evacuated sample containers using either mechanical or electrical pumps. In those instances, such as when sampling strata gases in drill holes, where mathematical calculations may be required for proper sampling, example calculations are provided. The use of a third type of evacuated container, which is generally restricted to non-specific sampling of the ambient mine atmosphere, is also described.

Jan 1, 1986

The creation of an accurate and precise enough database requires the collection of many representative samples, subsampling and assaying them without destroying their integrity. The selection and implementation of sampling protocols often lead to severe difficulties, making the unaware practitioner vulnerable to massive exploration, feasibility and operating failures. Therefore, there is no substitute for a good understanding of the Theory of Sampling (TOS). Either because of ignorance, or because of short-term economic reasons, failure to comply with many stringent conditions can be devastating for the validity of a feasibility study, leading to false evaluations, to the abandonment of a viable property, or to the exploitation of one that is not profitable. All these failures have something in common: insufficient duplicate sampling, subsampling and assaying were performed; very important, preliminary heterogeneity tests were never performed; for short-term economic reasons, poor sampling and subsampling protocols were selected, second-class sampling systems and weightometers were installed and poor preventive maintenance and cleaning were implemented;and sampling experts were called only when the project was obviously in trouble. The conclusion is clear: in due time, iterative auditing of sampling practices is an insurance policy against massive financial losses for the company involved. A complete, comprehensive 100 point checklist of sampling Quality Assurance and Quality Control is provided, so critically important details to ensure success are not forgotten.

Mar 1, 2010

By M Castello, P Bouchard, M Sabsabi, A Blouin, B Nguegang Kamwa, M Constantin, K Fytas, M Laflamme, K Rifai, F Vidal

Canada’s Quebec province operates 26 metal mines, 19 of which are producing gold and generating over C$1.8 billion (US$1.3 billion) annually. In all those mines, numerous samples of rock are collected and analysed daily in laboratories in order to determine their gold content. However, laboratory results are generally available within 48 hours of sampling (depending on the number of samples received by the laboratory and the priority of the results), thus potentially slowing down mining activities. In an attempt to find a solution for delayed laboratory results, Laval University, in partnership with the National Research Council Canada (NRC), the National Institute for Scientific Research (INRS) and four mining companies (Agnico Eagle, Canadian Malartic, Hecla Québec and Iamgold) have launched the Laser-Induced Breakdown Spectroscopy (LIBS) project for in situ real-time mining sample analysis. This work aims to minimise the amount of LIBS point sampling necessary to be representative of a full scan of the analysed area. Eight sampling patterns are investigated. A statistical analysis of the data was performed by varying the pattern steps. Results show that two of the patterns, 1 and 5 (single diagonal crossing the stratification of the samples with opposite slopes) minimise the amount of point sampling while remaining representative. The most successful results are obtained from steps which are greater than 6. In terms of the type of material, pattern 5 and steps 9 are better for core sampling, while pattern 1 and steps 8 are better for rock sampling. The geological explanation for these results is the heterogeneous distribution of gold in the sample which seems to be more homogeneous in the rocks than in the core sample due to the small rock size. In terms of the mineralisation, pattern 5 is better for mixed sulfides and quartz veins, and steps 8 and 9 are better for quartz veins.CITATION:Nguegang Kamwa, B, Rifai, K, Laflamme, M, Constantin, M, Castello, M, Vidal, F, Bouchard, P, Fytas, K, Blouin, A and Sabsabi, M, 2017. Sampling strategy by statistical analyses of LIBS data in the context of a portable gold analyser, in Proceedings Tenth International Mining Geology Conference 2017, pp 221–232 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 20, 2017

By F F. Pitard

The optimisation of a metallurgical plant requires two kinds of sampling systems. The first provides samples to follow process changes, so in due time actions to maintain key parameters within pre-established specifications can be applied. The second provides composite samples at the end of a working shift to demonstrate that valuable constituents sent to the plant went to either a final concentrate or to the tailings in a logical, expected proportion. Contrary to what many experts may claim, these two kinds of sampling requirements are incompatible and cannot be fulfilled using the same sampling systems; this is because the required samples have completely different objectives. A process control sample is an instantaneous sample with increments taken at short intervals, and the objective is to know where the process is at any given time. Excellent accuracy is not a must, however good precision is recommended since a possible process correction may need to be made. Online analysers fed with in-stream stationary cutters or other kinds of in-stream or by-stream probes can do well for such a mission. A composite sample for metallurgical accounting with increments collected over a long period of time, for which excellent accuracy and good precision are both required, must be generated by sophisticated and extremely well-maintained cross-stream sampling stations. The use of process control sampling systems to achieve good metallurgical accounting has been promoted by several manufacturers of sampling equipment around the world; it is a grave mistake that is misleading top management and a practice that should be scrutinised and preferably eliminated. Furthermore, bias tests used to prove otherwise are often based on insufficient data to draw valid conclusions; a guideline is provided.CITATION:Pitard, F F, 2014. Sampling systems for process control and metallurgical accounting, in Proceedings Sampling 2014 , pp 25–32 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

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.

Jan 1, 2005

"SAMPLING: THE IMPACT ON COSTS AND DECISION MAKING1. INTRODUCTIONFor years those in the minerals industry have been taking samples to assist them in making decisions; whether they be about investing billions of rand in a new mining operation, or simply whether a truck load of mineralised material should go to the mill or the waste dump, - we need a sample to help us make our decisions. This paper emphasises the importance and significance of sampling as a process, some of the associated technical problems and some of the advantages, especially in the light of traditional approaches that has usually left mine sampling to the least educated and lowest paid individuals on the mine. Perhaps the most important aspect that requires attention is the variability, usually measured in terms of precision, that tends to infect the sampling procedures. Francis Pitard 12 has stated: ""There is no such thing as reliable feasibility studies, unbiased ore grade control, accurate environmental assessments or effective process control, if you cannot identify and minimize the seven (ten) major sources of sampling variability. "" It is these ten sources of sampling variability and the way they arise that are briefly investigated. Variability is measured in terms of variances and rather than being self-compensating, there is good evidence to indicate that they are in fact additive at every step in the sampling and analytical process."

Jan 1, 2014