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By K H. Esbensen, K Engström

The conclusion from the debate over the last decade in the mining sector is that, for grade control, reverse circulation (RC) drill sampling gives more reliable results due to less sampling problems than blasthole samples. However, by conducting the latter with well-controlled procedures there still exist fair possibilities to counteract the major blasthole sampling problems and achieve fit-for-purpose representativeness, especially if based on minimal loss of fines. In the present PhD study, extensive replicate experiments and variographic characterisations are conducted to evaluate the performance of manual blasthole (BH) sampling and sectorial segment sampling. The experiments were performed in Luossavaara-Kiirunavaara AB (LKAB)’s Leveäniemi open pit mine in the north of Sweden. The experimental area is an apatite-magnetite iron ore deposit of the Kiruna type, with the demand to exhibit a maximum of local heterogeneity to allow the experiment the largest possible conclusion powers. Gathering and splitting the complete BH drill cutting cones (fines cone, coarse cone) served as an authoritative reference on which to assess the representativity of the two BH sampling methods.Results show that manual BH drill sampling is able to produce samples with an acceptable accuracy and precision if performed properly and with insight. Based on variographic comparison, segment sampling shows similar precision for chemical grades as the manual sampling, while the particle size distributions were more deviating for segment sampling than for manual sampling at the high and low size ranges. Possible reasons for these partly surprising results are discussed.For further investigation of the performance of BH versus RC drill sampling, a second experiment has been planned on a parallel profile. This will result in an unprecedented opportunity for a full variographic comparison between RC and BH sampling, aimed at designing specific, fit-for-purpose sampling procedures as a function of ore quality, cut-off grades and important contaminant levels.CITATION:Engström, K and Esbensen, K H, 2017. Blasthole sampling (replicate and variographic experiments) in LKAB open pit iron ore mines – fit-for-purpose representativity?, Australia, in Proceedings Eighth World Conference on Sampling and Blending , pp 85–96 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By R Shaw, M Hidding

The design of primary sample cutters for high-capacity conveyors is critical to achieving a correct sample, both in collecting the sample and ensuring smooth sample discharge flow. Modelling usually occurs at the design stage and requires extensive experience to achieve the correct result. However, it is difficult to confirm the effectiveness of the cutter design until after installation. Even with high-speed video, confirmation of complete sample capture is difficult, especially if the material characteristics change over time. Recent investigation using discrete element method (DEM) simulation to model the flow of the ore into the cutter and observe how it interacts with the cutter has revealed some interesting design requirements not observed in the past. By including DEM modelling, the correct design and optimisation of the cutter can be more accurately determined prior to commissioning.CITATION:Hidding, M and Shaw, R, 2017. Modelling ore flow in the design of high-capacity sample cutters, in Proceedings Eighth World Conference on Sampling and Blending , pp 145–150 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By R J. Holmes

The procedures and equipment used for sampling iron ore process streams have generally improved in recent years. However, there are a number of areas where significant issues still commonly occur, including sampling high capacity streams, sample cutter design and operation, division of primary increments at the secondary sampling stage, the performance of crushers installed to reduce the particle size prior to sample division, and the sample mass that is retained at each sampling stage. These issues of non-conformance with correct sampling principles are caused by factors such as increasing the speed and loading of conveyor belts to increase throughput, which changes the trajectory of the ore stream and may result in an inability of the sample cutter to handle the increased flow rate without sample loss. Failure to sample the entire primary increment stream at the secondary sampling stage is also a common issue as well as failure of crushers to provide crushed products of the required particle size due to ongoing maintenance and/or adjustment issues. These common pitfalls can all be addressed through careful attention to detail.CITATION:Holmes, R J, 2017. Common pitfalls in sampling iron ore, in Proceedings Eighth World Conference on Sampling and Blending , pp 261–264 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By M P. Cipold, J F. Bachmann, C C. Bachmann, J Ha

Knowledge of fuel qualities is of ultimate importance for the efficiency of chemical plants or power stations. Although most blending yards are equipped with automatic samplers and the derived samples are processed in the laboratory, the quality variation of the blended product may still be strong. This is caused by several reasons. First of all the process of sampling, crushing, dividing and analysis is time consuming. Results are available only with a delay of two to 24 hours. During this time blending is operated without knowledge of the main parameters. Conveyors with high throughput require expensive sampling systems capable of handling samples which are representative. The frequency of sampling needs also to be high in order to be representative. This results in high maintenance costs and also downtime of the sampling system.Online analysis of bulk materials has matured over time and today it can supplement but not replace laboratory analysis. As an example, J&C Bachmann has realised a coal quality monitoring system on the coal supply of a South African coal processing plant. The plant consumes ~42 Mt of coal per annum. The blending yard consists of two sets of three heaps where coal of six different mines is blended according to the factory’s requirement. From there, the blended coal is fed to the factory on two different belts.In 2010 the plant installed a TEXAS X-ray fluorescence analyser as a trial. It was tested under different conditions on several belts before the customer decided to install six TEXAS analysers on the feed belts to the stackers and two more analysers on the reclaimer belts. All analysers are installed directly on the belt and not on a substream.During the test period blending strategies were developed in close cooperation between the customer and J&C Bachmann. The use of the analysers in conjunction with the blending model has proven that the variation of the reclaimed coal quality was decreased significantly, which results in increased process performance and significant cost savings.CITATION:Bachmann, C C, Bachmann, J F, Cipold, M P and Ha, J, 2017. Online quality control of a coal blending yard, in Proceedings Eighth World Conference on Sampling and Blending , pp 125–128 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By R C. A Minnitt

The two methods for calibrating the parameters K and (alpha) for use in Gy’s equation for the fundamental sampling error (FSE), the duplicate sampling analysis (DSA) and the segregation fractionalised analysis (SFA) are described in detail. A case study using identical broken reef material from a Witwatersrand-type orebody is calibrated using the DSA and SFA methods and the results are compared. Classically, the form of Gy’s equation for the FSE raises the nominal size of fragments given by dN to a power of 3. Gy’s formula is a theoretical formula applicable in the case when fragments are randomly collected one by one, which is obviously never the case. This is because the fragment shape factor (f) and the granulometric factor (g) were introduced to take care of this anomaly. More recent formulae, such as those suggested by François-Bongarçon and Lyman, are empirically based formula. A later modification of Gy’s formula raises dN to the power ? (alpha), the latter term being calibrated with the coefficient K in the DSA and SFA methods. The preferred value of ? (alpha) for low-grade gold ores used by sampling practitioners in the mining industry is 1.5. A review of calibration experiments for low-grade gold ores using the DSA and SFA methods has produced values of K that vary between 70 and 170 and values of alpha in the range 0.97 to 1.30. The average value for alpha is found to be 1, rather than 3 as originally proposed in the classic form of Gy’s equation or the industry preference for 1.5. It is suggested that for low-grade gold bearing ores, the equation for the FSE should raise dN to a power of 1. Such an equation for the variance of the combined FSE plus the grouping and segregation error, now referred to as the short-term component of the quality fluctuation error (QFE1), greatly simplifies the characterisation of gold ores, which now only requires the calibration of K for a given mass and established fragment size. The implications for the heterogeneity test from the generalised equation are that provided the fragments have been screened to within a narrow size range, any particular size will return a value for K that is acceptable for use in the sampling nomogram.CITATION:Minnitt, R C A, 2017. A generalised form of Gy’s equation for gold ores – empirical evidence, in Proceedings Eighth World Conference on Sampling and Blending , pp 331–350 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By Y Xie, L O’Connor, S C. Dominy

Sheeted vein gold deposits are characterised by multiple subparallel veins and often free milling gold. Variability in gold grade and recovery parameters are enhanced by poorly designed sampling and test work protocols. Poor quality samples generally equate to an enhanced nugget effect. A sample can be described as being representative when it results in acceptable levels of bias and precision. Total sampling variability can be quantified by the relative sampling variance, which measures the total empirical sampling variance influenced by the heterogeneity of the lot being sampled under the current sampling / test work protocol. Effort should be made to minimise the relative sampling variance through Theory of Sampling (TOS) and quality assurance / quality control (QA/QC) application. This contribution reports on a case study that exemplifies how high-quality grade and recovery data can be gained from a well-designed and planned drilling, sampling and test work program, followed by bulk sampling. Results across historical resource drilling, geometallurgical drilling, laboratory assay / test work, geometallurgical modelling and a bulk sampling/pilot processing study are reconciled. A whole core, entire-sample test work protocol was used to acquire fit-for-purpose gold grade and recovery data for inclusion into a Pre Feasibility Study reported in accordance with the JORC Code.CITATION:Dominy, S C, O’Connor, L, Xie, Y and Glass, H J, 2017. Geometallurgical sampling protocol validation by bulk sampling in a sheeted vein gold deposit, in Proceedings Eighth World Conference on Sampling and Blending , pp 185–196 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By G J. Lyman

Sampling of grain for mycotoxins is challenging in that some mycotoxins, such as Ochratoxin A (OTA), have very low permitted levels and the kernels carrying the mycotoxin may be highly localised within the bulk of the grain as ‘hot spots’. Sampling for genetically modified organisms (GMOs) can be fraught with similar problems, especially if the contamination is adventitious and random. Incremental sampling under these circumstances can be shown to lead to a high variance in the sampling result.A new design of sampler for grain and other free-flowing particulates has been developed that is effective in practically eliminating the sampling variance due to distributional heterogeneity (DH).This paper introduces the design and argues that the sampler should be free of sampling variance due to DH.CITATION:Lyman, G J, 2017. A new sampler for grain and other free-flowing particulates, in Proceedings Eighth World Conference on Sampling and Blending , pp 277–280 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By M D. Sussman

A large number of countries in the world now have some regulation for labelling the presence of genetically modified organisms (GMO) in food. For the last ten years, ISO/TC 34/SC 16 has been providing methods, guidelines and requirements concerning the evaluation of molecular biomarkers including GMOs. This activity makes sure that stakeholders on all sides of commercial transactions are satisfied and their marketing or regulatory needs are met. Developing and maintaining a transparent, science-based environment for users of horizontal methods for molecular biomarker analysis is a high priority. Sampling and analysis of molecular biomarkers has become critically important for food and agricultural organisations developing, producing, distributing and selling genetically engineered, non-genetically engineered, identity preserved and/or organic crops and their derived products and preventing their respective destructive diseases. To aid in international harmonisation, a new work item proposal to develop an ISO type A management system standard (MSS) for organisations including laboratories using molecular biomarker analysis and sampling has been submitted to ISO/TC 34/SC 16. The purpose of this MSS is ensuring that determination of the presence, absence or quantification of a particular biomarker with a relationship to a product quality determinant, a marketing claim, a regulation or a plant pathogen is carried out under a suitable quality assurance / quality control system. This MSS will provide an overarching system to apply and manage molecular biomarker analysis and sampling for any organisation large, medium or small in a private, government or academic setting.CITATION:Sussman, M D, 2017. ISO/TC 34/SC 16 horizontal methods for molecular biomarker analysis – molecular biomarker analysis and sampling management system, in Proceedings Eighth World Conference on Sampling and Blending , pp 295–302 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By P Minkkinen

"Pierre Gy showed in his early works that the results in estimating the variance of the mean value of continuous lots depends on sampling mode (random, stratified or systematic), except in the theoretical case that the measurand is randomly distributed in the lot. The experimental variance estimate calculated from samples taken from the lot is independent of sampling mode but depends on sample size and the type of the lot heterogeneity. Variography can be used to estimate the variance of the lot mean for different sampling modes.Gy also defined weighting error, which is made if a simple average is calculated from the assays of samples taken from a continuous object (eg from a process stream) where the flow rate varies with time and is correlated with the concentration of the analyte in the stream.The effect on sampling uncertainty of the three sampling modes and their interaction with the following factors in estimating the uncertainty of the mean concentration is studied using simulated lots, and lots having heterogeneity patterns similar to those of real data sets, including the effect of: the type of heterogeneity (random, grading and clustered heterogeneity patterns) increment size and number employing composite sampling weighting error. CITATION:Minkkinen, P, 2017. Dependence of the variance of lot average on the sampling mode and heterogeneity type of the lot, in Proceedings Eighth World Conference on Sampling and Blending, pp 319–330 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

May 9, 2017

By A C. Chieregati, A Heberle, F L. S P Villanova

Since the publications of Gy’s initial work, many authors have discussed the approaches to estimate the variance of the fundamental sampling error (FSE). Experimental tests suggested by Pitard and François-Bongarçon determine the sampling parameters K and a to calibrate Gy’s formula. The heterogeneity test and the sample tree method are well known and broadly used for many commodities. However, prior to the analysis, the collection of a representative sample is an intriguingly difficult task. The composite for the heterogeneity tests often consists of run-?of-?mine material or processed ore on a conveyor belt. In most cases the final sample does not have enough mass, mixes different geological units, represents a rare blend and does not characterise the deposit. In order to obtain a reliable composite for trace elements and low-grade ores, sampling practitioners suggest at least half a ton of material.To prevent the common representative pitfalls in the FSE, this case study focuses on a single gold-?bearing geological unit collected in situ. With this unit being structured by the mine geologists, but with the interaction between engineers and chemists, it was possible to validate the reconciliation suggested by Pitard (2015). The heterogeneity test and the sample tree method were performed using 500 kg of a smoky quartz from a single geological domain and the results were compared to Gy’s formula. The logging practice by the geologists was modified, emphasising the definition of the size of the gold nugget or its cluster, number of grains and association. Finally, a mineralogical study was conducted on the geological unit to define the liberation factor. All the information is compiled in this paper, showing the need to fully understand the mineralisation to correctly define representative samples and proper analytical techniques.CITATION:Villanova, F L S P, Heberle, A and Chieregati, A C, 2017. Heterogeneity tests and core logging – a final reconciliation, in Proceedings Eighth World Conference on Sampling and Blending , pp 107–114 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By M Singh, R C. Steinhaus, J J. Rust

Metallurgical process control systems in mineral plants often employ state-of-the-art monitoring analysers in an attempt to optimise plant operations and enhance mineral recoveries. These systems are unfortunately invariably fed by simplistic ‘sampling’ designs consisting of various in-stream methods or pressurised probes. These are unfortunate compromises and provide a false picture of what a mineral flotation or other process can achieve. The situation is further aggravated by some manufacturers using misleading marketing to suggest discrete cross-stream methods for metallurgical accounting and unreliable continuous in-stream off-takes for control sampling and that they are of synonymous integrity.Reliable, absolute or representative samples are often absent in many modern day concentrator plants, due to compromises around sample taking, particularly in terms of delineation and extraction errors and thus leading to sample bias. For more reliable information from online analysers we need a continuous, but much more reliable sample taken, than is current practice.Online analysers require a fully proportional and continuous supply of sample for effective process control, otherwise maximised or benchmark recoveries will be nothing but a pipe dream. Large discrete cross-stream samples from mechanically less reliable equipment cannot be used as feed to online analysers, as it will negatively impact real-time feedback and efficient process control.It is possible to build correct sampling systems that could do both process controls online and metallurgical accounting at the same time. Multotec Process Equipment (Pty) Limited, in close association with experienced sampling consultant Francis Pitard, have jointly patented a concept to achieve this and which is embodied in the design of the unique Rotating Cone® Sampler (International Patent Application No. PCT/IB2016/057464). This versatile unit for dry and wet applications has high division ratios via two-step sampling and division.A prototype was built and tested to validate the design and levels of precision achievable when employing the unit to split dry mineral fines or even certain foodstuffs.CITATION:Steinhaus, R C, Rust, J J and Singh, M, 2017. Does process control sampling always have to be a compromise?, in Proceedings Eighth World Conference on Sampling and Blending , pp 197–202 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By S C. Dominy

This contribution examines the effect of feedstock sampling before the sorting stage of polyethylene terephtalate (PET) bottle recycling. Batches of waste PET bottles may contain non-PET bottles which need to be removed by sorting. Any residual presence of PVC is detrimental for the quality of products manufactured from recycled PET. The maximum tolerated concentration of PVC in cleaned PET is extremely low, which places high demands on the recovery of PVC achieved by sorting. To be confident of attaining the desired PET quality after sorting, acceptance sampling of truckloads of PET bottles may take place prior to sorting. It is shown that accounting for sampling uncertainty requires that the sorting process achieves a consistently high recovery of PVC bottles.CITATION:Glass, H J and Dominy, S C, 2017. Plastics recycling and sampling, in Proceedings Eighth World Conference on Sampling and Blending , pp 129–134 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By N H. Jansen, H L. Bridgwater

Field portable X-ray fluorescence (XRF) and near infrared (NIR) instruments are proven technologies in the minerals industry. The in-field results of these instruments can add value early in the project life, leading to cost-effective decision-making compared to their equivalent laboratory based techniques. However, in order to add value to a project, it is crucial that the instruments are employed correctly.Orientation studies should be carried out prior to initiating a sampling program to achieve the highest level of quality assurance and control for the results. Hence, an appropriate sampling workflow begins with considering what outcomes are required, including level of precision, accuracy, reliability for elements and minerals of interest, and sampling errors. The workflows proposed include soil sampling and reverse circulation and diamond drilling sampling techniques, and laboratory based techniques. They are practical and flexible workflows using available portable XRF and NIR technologies that can be applied to current sampling and drilling methods.The combined technologies have the ability to create large, integrated geochemical and mineralogical data sets that can provide cost savings for every stage of the mining chain from exploration to prefeasibility, mineral processing and environmental remediation.CITATION:Jansen, N H and Bridgwater, H L, 2017. Proposed workflows for portable X-ray fluorescence and near infrared instruments for soil and drill hole sampling, in Proceedings Eighth World Conference on Sampling and Blending , pp 219–228 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By A Hollweg, K H. Esbensen, M Lischka

Two new splitter designs for intercepting moving streams of matter are presented, the variable cross-stream splitter (vCSS) and the rotary stream splitter (RSS), which are intended for implementation in a fully automated modus. The vCSS facilitates obtaining up to three subsamples with different splitting ratios from automated laboratory flow paths, while the RSS divides the intercepted sample into 16 equal analytical aliquots. The splitters can process a wide range of dry, granular and free-flowing materials (<5 mm). The required weight for each subsample can be set without mechanical adjustments in the case of the vCSS. Typical applications are subsampling/splitting for X-ray fluorescence pelletisation (or fused beads) or splitting for average shift composite sampling or grain size analysis. The vCSS combines the advantages of riffle splitting (three user-defined subsample masses) with miniature bed blending and can accept up to 15 kg in total incoming sample mass. In practice, the automated vCSS can deal with unlimited ingoing mass fluxes, while the RSS is designed to operate at a user-defined fixed subsampling rate (1/16th in the tested version). Both splitters have the potential to be optimised and evaluated according to an application/user-defined total sampling error (TSE) + total analytical error (TAE) threshold. Both splitters were tested under adverse conditions and with compositions involving three components with properties representing typical mass fluxes and components concentrations in industrial and technological systems: phosphate concentrate 89.00 per cent (<500 µm), slag ten per cent (>2 mm) and glass spheres one per cent (750–1000 µm). The slag particles vary widely in particle shape, aspect ratio and surface roughness, while the glass spheres are extremely prone to bouncing and segregation (spillage). This test system is extremely difficult to split and thus constitutes a realistic worst-case scenario. While the vCSS does not comply strictly with Theory of Sampling demands for correctness, both splitters may well be able to deliver fit-for-purpose representative results if/when tested properly. Average relative mass deviations ranged from 1.89 per cent for phosphate concentrate (relative standard deviations of 20-fold repeated splitting) to 4.72 per cent for glass spheres and 13.04 per cent for slag. For the RSS, the average replication experiment splitting variation was 2.89 per cent for phosphate concentrate, 4.82 per cent for glass spheres and 11.1 per cent for slag. Both new automated online splitters would therefore appear to be able to be tuned to fit-for-purpose representivity (defined here as maximum relative reproducibility <5 per cent) for many non-extreme materials; however, it is always imperative to test every new type/class of material empirically. There is a wide commercial potential for important materials and commodities sectors, including food, feed, drugs (pharmaceutical), secondary raw materials, recyclates, agricultural products and seeds.CITATION:Lischka, M, Hollweg, A and Esbensen, K H, 2017. New online/atline splitter designs for laboratory automation – feasibility results, in Proceedings Eighth World Conference on Sampling and Blending , pp 159–166 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By B Chi, A Mahanta, R A. M Maddever, O Dominguez

BHP Billiton, in collaboration with Sodern and CSIRO, has developed a downhole logging tool for measuring geochemistry in situ. The FastGradeTM 100 (alternatively, FG100), exploits pulsed fast and thermal neutron analysis (PFTNA) to analyse the rock. This method is based on the detection and measurement of characteristic gamma rays emitted from isotopes in the material that become briefly radioactive when they are bombarded with neutrons. The tool outputs raw spectra arising from three types of neutron interaction, namely, inelastic scattering, neutron capture and neutron activation, which occur in successive time-windows (Smith et al, 2015). The spectra contain peaks at specific energies that are characteristic of the different elements interacting with the neutrons, the gamma count of which is related to elemental concentration. BHP Billiton uses in-house data processing software to convert the measured spectra to elemental concentrations.Much work has been done to understand the tool response under a wide variety of environmental and operating conditions, including logging speed, borehole rugosity, the presence of cavities, water etc. Effort has also been directed at understanding and quantifying the current sources of error in our conventional assays due to sample collection, preparation, and laboratory instrumentation in order to use this information as a baseline to compare against the FastGradeTM 100 results.To date, the FastGradeTM 100 primary use has been as a verification tool (JORC, 2012). However, beyond this, it has considerable potential to improve productivity in both exploration and production environments due to its ability to provide geologists with assay results soon after a hole is drilled.This paper presents initial test work and tool performance, including accuracy and precision under various conditions. Results are illustrated by comparing tool measurements to assays from laboratory samples collected from both reverse circulation and diamond-drilled holes.CITATION:Chi, B, Maddever, R A M, Mahanta, A and Dominguez, O, 2017. Near real-time assay with downhole assay tool (FastGrade™ 100), in Proceedings Eighth World Conference on Sampling and Blending , pp 137–144 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

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 S C. Dominy

Various styles of gold mineralisation pose problems during sampling because of their complex gold grade distribution and presence of coarse gold. Effective sampling and sample preparation forms the basis of resource estimates. The design of a sampling and assaying protocol must be based on a thorough understanding of the mineralisation in question, which should include consideration of gold particle size and mass distribution. The proportion of coarse gold within samples has a profound effect on the requirements for sample preparation and assaying. Choice of preparation procedure and assay type is of critical importance if sampling errors are to be minimised. Traditional fire assays, using small charge sizes, often understate assays gained from large charge sizes and techniques such as screen fire assay and LeachWELL. Proper application of the Theory of Sampling reduces errors during collection, preparation and assaying. A quality control/quality assurance program must be implemented and critically assessed. A case study from the Ballarat mine is presented, which is characterised by substantial quantities of coarse-gold hosted in quartz veins. Diamond core drilling is the key input into resource delineation. A number of different sampling and assay options have been trialled over the recent project history. The current sampling protocol utilises whole drill core, using LeachWELL assays after logging and photography.CITATION:Dominy, S C, 2017. Sampling coarse gold-bearing mineralisation – developing effective protocols and a case study from the Ballarat mine, Australia, in Proceedings Eighth World Conference on Sampling and Blending , pp 71–84 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By C Haughy, F F. Pitard

Blue Mountain Minerals is a large supplier of calcium carbonate products used by various manufacturers as a major raw material. Very small amounts of free carbon in the rock can have a huge impact in carbon-sensitive applications. The marble in the deposit at Blue Mountain Minerals contains variable amounts of free carbon in the form of trace amounts of the mineral graphite.To make quality improvements, variography was used to measure the impact of non-ideal sampling practices at the Blue Mountain Minerals operations in Columbia, California in the United States. A major investment in correct sampling systems and sampling protocols was implemented over a one-year period. Subsequent variography was used to measure the improvement on the predictability of the graphite content in marble produced by the quarry and as it is processed as an acceptable raw material for extremely carbon-sensitive applications. The improvements of the sampling protocols, control of long-range changes and better knowledge of process cycles are easily noticeable on variograms when they happen to exist. The economic impacts are discussed.CITATION:Haughy, C and Pitard, F F, 2017. Use of variography to measure improvement of sampling practices for the quality control of graphite consistency in marble used for carbon-sensitive applications, in Proceedings Eighth World Conference on Sampling and Blending , pp 255–260 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By D A. Vogel

"Sampling may be mandatory for safety or environmental reasons. It may be because the value of a sale needs to be determined or to provide important data for investments. Whatever the reason, an incorrect sample will come at a risk. Only the person that relies on sampling data can imagine what the potential cost of such risk is.Sampling itself has a cost too; the direct cost of a quality control department and indirect cost for interrupting a process, such as stopping a conveyor belt during loading. If all material is the same then only one small sample is enough. But if all the material is different, then a very large sample must be tested; in the conveyor belt scenario it must be stopped very frequently and the associated cost will be high.A good sampling plan finds the proper balance between risk and cost. A good sample must be small enough to handle yet large enough to be representative for what information is required. All good samples have three things in common: there was proper access to the material the sampling tool allowed equal probability of particles being selected nothing was disregarded, everything was included.This too is the core of Gy’s ideal sampling scenario, where all particles have the same probability of being selected and included in the sample. There is risk of bias when part of a lot is excluded from the sample. Extra care is needed when sampling particularly heterogeneous, segregated or layered materials. The sampling tool should never systematically leave out a portion; it shall not push aside large or hard particles. Wet samples shall not stick to the sampling tool. This extends to sample preparation too when reducing particle size and dividing the sample in the process.CITATION:Vogel, D A, 2017. Access, select, include – a review of the commercial sampling of traded bulk commodities in the context of Gy’s Theory of Sampling, in Proceedings Eighth World Conference on Sampling and Blending, pp 367–378 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

May 9, 2017

By F F. Pitard

Observation from attending many conferences around the world reveals that many people are constantly shifting from the word ‘error’ to the word ‘uncertainty’ without a clear vision of what the subtle difference is. The same applies to a large swath of the scientific literature, in which these two concepts are unfortunately often used synonymously, which is a scientific flaw of the first order. In practice, as demonstrated in the Theory of Sampling (TOS), there are both sampling errors and sampling uncertainties. Sampling errors can easily be preventively minimised, while sampling uncertainty for a preselected sampling protocol is inevitable. P M Gy’s work, supported by G Matheron’s critical reviews, demonstrates beyond any possible doubt that sampling uncertainty cannot be achieved if sampling errors are not preventively minimised by applying very stringent rules that are too often ignored by sampling practitioners. A clarification, in line with the TOS, is presented to mitigate the confusion created by circles favouring the use of uncertainty versus the use of the word error. Both words are very different in nature and scope and they are both necessary in their due place and time.CITATION:Pitard, F F, 2017. From errors to uncertainty – a clarification for proper use by the Theory of Sampling, in Proceedings Eighth World Conference on Sampling and Blending , pp 355–358 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017