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By C Paoletti

About 15 years ago the debate on the reliability of sampling procedures for agricultural food and feed commodities was initiated by a few brave scientists who questioned the representativeness of what, at the time, were routine sampling procedures established exclusively on the basis of tradition and practical feasibility. Over time several national and international organisations contributed to the discussion, developing standards and publishing papers either to underline the fact that sampling is always a source of error, in itself, which cannot be overlooked, or to minimise the relevance of sampling on the basis of argumentations linked to time and money limitations. The latter dominated, but is nevertheless impossible to accept from both a scientific and a responsible management perspective. Here the author presents an historical overview of these past 15 years, illustrating the most controversial issues, reporting major achievements in food/feed sampling, highlighting milestones, as well as unresolved problems that still need the attention and contribution from the sampling community. The need to incorporate Theory of Sampling (TOS) into food/feed safety assessment (pre-market), as well as into food/feed safety monitoring (post-market) has recently been highlighted, and the practical applicability of TOS to base food/feed safety decisions on representative sampling, has been explored and documented via concrete examples of risk analysis in different areas of food/feed industrial production chains. Now it is time to move forward and ensure that TOS is fully integrated into the well-established Food and Agriculture Organization (FAO) / World Health Organization (WHO) risk assessment approach, in order to guarantee a transparent and correct universal frame for risk assessment and the subsequent management decision-making process. WCSB8 offers the ideal platform to explore the best way to do so, switching from a theoretical discussion of correct sampling principles for food and feed agricultural commodities to practical recommendations in the interest of consumers, worldwide. These 15 years have also been a personal journey of interesting dimensions, indeed a veritable roller-coaster ride in-and-out of the scientific, technological, industrial and (last but not least) regulatory worlds. The start was WCSB2 in 2004, when I flew to Australia to discover if in other areas sampling had been identified as an unavoidable issue and, if so, what the solution was. There, I found a good travel companion who today is still on my side, to assemble the puzzle and explore how to shape the future, to concretely answer to the 15 years’ quest for proper sampling in the assessment and monitoring of food/feed safety.CITATION:Paoletti, C, 2017. Assessing and monitoring food and feed safety – a 15 year quest for proper sampling, in Proceedings Eighth World Conference on Sampling and Blending , pp 39–44 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By D François-Bongarçon

The link between geostatistics and Theory of Sampling (TOS) is examined again in view of new findings made while trying to compare predicted sampling variances and estimated grades (using geostatistics) on the same material, and this provides an important consistency requirement whenever calculations are made that involve both theories in a comparative way. It also briefly explores the relationship to De Wijs fractal model of mineralisation and eventually provides new support for the use of consistency theory for calibrating TOS variance formulas.CITATION:François-Bongarçon, D, 2017. Theory of Sampling and geostatistics – the ultimate link, in Proceedings Eighth World Conference on Sampling and Blending , pp 391–394 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By A Parbhakar-Fox, S C. Dominy

The prediction of acid and metalliferous drainage (AMD) from mine waste materials is critically important during all stages of the mine value chain. However, to determine such geoenvironmental characteristics static and kinetic testing of individual waste units must be performed on representative samples. The importance of sample selection cannot be underestimated; it is the single most critical aspect of any geoenvironmental investigation. Poor sampling techniques and inadequate sample selection will contribute to excessive variance, difficulties in interpretation and incorrect assessment. By undertaking mesotextural characterisation based on hyperspectral mineralogical data (with sulfur assay data if available), opportunities to improve sampling strategies are presented. Such data will inform the selection of samples for low-cost total sulfur, paste pH and geoenvironmental logging, which collectively will allow for accurate AMD forecasting and improved sampling practices to be achieved. Once sampled, robust quality assurance/quality control (QA/QC) protocols must be developed where the use of international certified reference standards become obligatory. This is currently not practised, resulting in inter-laboratory discrepancies when undertaking waste classification. Further improvements in geoenvironmental characterisation testing lies in the adoption of blended static testing protocols, whereby blends of waste materials can be efficiently tested so as to give an early forecast as to whether they will produce acidic or metal-laden drainage. Such information is integral in developing a waste schedule that will allow for a non-acid forming waste landform to be constructed right from the start of the operational phase. Ultimately, improvements in this discipline lie in technological innovations with regards to mineralogical characterisation, and by offering geoscience and minerals engineering students a competent education in the discipline. Collectively, this will empower a step-change in AMD prediction and waste handling, and will make the realisation of the United Nations Sustainable Development Goals a reality, rather than a dream.CITATION:Parbhakar-Fox, A and Dominy, S C, 2017. Sampling and blending in geoenvironmental campaigns – current practice and future opportunities, in Proceedings Eighth World Conference on Sampling and Blending , pp 45–54 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By D W. Wall, J P. Vagenas

Questions of sampling precision and accuracy affect every process plant in differing ways and to differing extents. To address these perceived issues, chemists, metallurgists and engineers are often asked to check their results to confirm their reliability. However, it is not possible to achieve the level of detail required to check every sample, especially if the event has already occurred. No operation has the resources to frequently sample every point in the process for the purpose of verification.One way to address this scenario is by generating a sufficiently detailed comparative data set from a dynamic mass balance simulation of the processing plant. This mass balance uses both sample and plant instrument data to model every flow, while also taking inventory changes and process chemistry into account.The mass balance data can be used to identify any bias present in plant sampling and analysis. It can also be used to verify any unexpected assay results by taking a holistic view of the process. Improving the precision and accuracy of sampling data helps support operational decision-?making and understanding. It also improves the accuracy of the dynamic mass balance used in this process, facilitating further improvements.CITATION:Vagenas, J P and Wall, D W, 2017. The benefits of automated metallurgical accounting for plant sampling, in Proceedings Eighth World Conference on Sampling and Blending , pp 203–210 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By O Bøckman, E Thisted, K H. Esbensen, U Thisted

Process industry measurement systems, once installed, are often interpreted as static black boxes delivering correct values as long as not proven otherwise. After first implementation and set-up with a distinct sampling frequency, they often run ‘forever’ without checking if this frequency is still appropriate with respect to changed raw materials characteristics, different process conditions or changed customer specifications. Failure detection is based on operator experience and skills (often close to an art form) and/or from control measurements of reference samples. This may well be too late for many industrial process contexts. An appropriate sampling frequency is a critical success factor for valid monitoring and control.Based on the methodology of variographic analysis we here illustrate how to design/re-design/optimise an industrial measurement system in terms of sampling frequency, discretisation and accuracy based on customer specifications. We also show how variographics can be used to monitor for deviations. We use experience from process industry to show how these methods can be used not only for product characterisation, but also for monitoring input factors, process indicators and measurement of these. The information hidden in the vast amounts of data that is continuously created in modern process industries can be utilised to cut costs in the measurement activities and at the same time contribute to early error detection. We illustrate by case examples from production processes at a nickel refinery, supporting the Lean and Six Sigma methodology. This will provide a standard operating procedure for monitoring the stability of measurement systems, and provide a basis with which to discuss how a variographic approach can be integrated with the Lean and Six Sigma approach as an online tool to map, characterise and validate measurement systems and thus avoid more cumbersome measurement system analysis in full detail.CITATION:Thisted, E, Thisted, U, Bøckman, O and Esbensen, K H, 2017. Variographic case study for designing, monitoring and optimising industrial measurement systems – the missing link in Lean and Six Sigma, in Proceedings Eighth World Conference on Sampling and Blending , pp 359–366 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By R J. Romañach

The theory of sampling (TOS) is now followed in the development of near infrared (NIR) spectroscopic methods for the pharmaceutical industry. TOS has been the guiding light for a two-step approach for spectroscopic composite sampling available for the development of real-time analytical methods for particulate-based industrial processes. The two-step spectroscopic sample composite has been used in the development of process analytical technology (PAT), an important regulatory initiative to improve the quality of pharmaceutical products. The two-step sample composite approach overcomes the low sample volume analysed by PAT methods; one of the key limitations to PAT implementation in the industrial production of particulate-based materials.PAT methods need to be validated demonstrating their accuracy, precision, specificity and robustness. TOS provides a deeper understanding of these validation studies which involve discrete particulate material. The validation of PAT methods is now being performed considering both sampling and analytical errors, while practically all previous method validations only addressed the total analytical error (TAE) of these methods. This has been a crucial impediment for the field of chemometrics as well.PAT is a key element of continuous manufacturing (CM) processes, which are considered an emerging technology capable of increasing the flexibility, agility and robustness of pharmaceutical processes while reducing their costs. The outflow of the continuous process is a 1D lot, and variographic analysis has been shown to discern process variation from sampling and analytical errors in pharmaceutical CM processes. Thus, variographic analysis is a process improvement tool with significant potential for improving CM processes. The possibilities for improving pharmaceutical manufacturing are enormous.CITATION:Romañach, R J, 2017. Theory of Sampling – from missing link to key enabler for process analytical technology, in Proceedings Eighth World Conference on Sampling and Blending , pp 63–68 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By B W. Atkinson, J Docherty

An Australian Working Group has been compiling a guide for operators to use in order to improve the efficacy of their routine and non-routine sampling tasks within their coal preparation process. The guide focuses on unit operations within the process, and not conveyor belts or transfer stations that are adequately covered by the existing Standard ISO 13909.Representative sampling of unit operations is generally an after-thought following plant construction, whether that be a result of oversight, or more commonly, capital cost minimisation during initial construction. Minimising capital cost by avoiding adequate sampling systems is generally a poor decision, since the result is a process that cannot be adequately measured, meaning that it cannot be adequately managed and optimised.The use of the term ‘guide’ rather than ‘standard’ is intentional. Practical means of sampling unit operations within existing plants generally relies on a compromise between sample representativity (minimisation of bias) and sampling feasibility. The guide does not endorse poor sampling practice, but instead offers practical guidance to overcome, to the extent reasonably possible, real-life limitations.When it comes to sampling any particular process, it is almost always necessary to make a professional judgement as to whether or not the proposed samples are likely to be sufficiently representative such that the sampling will be worthwhile. Such judgement needs to be exercised by personnel who understand the principles of sampling, as they apply to the stream being sampled. There are many circumstances where available methods for sampling of a particular stream will be meaningless, and in such circumstances analytical data from that sampling will only serve to propagate poor decision-making.This paper outlines the approach for the guide, the areas it covers and the approaches adopted for different types of unit operations and associated equipment configurations.CITATION:Atkinson, B W and Docherty, J, 2017. A practical guide to sampling in coal preparation plants, in Proceedings Eighth World Conference on Sampling and Blending , pp 241–246 (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 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 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 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 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 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

By G Gilchrist, S C. Dominy, T Jashashvili, R C. A Minnitt

The grouping and segregation error is one of Gy’s seven errors that cannot be measured in any qualitative manner because of its transient and changing characteristics each time the sample is handled mechanically. Attempts to calculate the mean and variance of the segregation error is considered by some as ‘an exercise in futility’ that would not help us achieve representative sampling. This research is aimed at understanding the process of segregation of dense, finely divided and dilute particulate materials supported in a clean quartz substrate during intense agitation. Several mixtures of fine-grained materials including steel, tungsten carbide and gold grains were used in these experiments. Four hundred grains of each of the minerals and metals at a top size of 2 mm were mixed with approximately 600 g of finely ground quartz material (95 per cent passing 2000 microns) in a 500 ml plastic jar with a sealable lid. The mineral–?metal mixtures were analysed in three steps using microfocus X-ray computed tomography. Step one consisted of rolling the containers on a flat surface in an attempt to concentrate the mineral and metal grains in the core of the supporting substrate; this is a base case that can be reconstructed after each session of analysis. The second step involved gentle and vigorous shaking of the mixtures in order to segregate the denser minerals and metals from the supporting quartz substrate. In some cases, a third step involved splitting the sample through a riffle splitter. Three-?dimensional images of the surface of each grain, its volume and its X-, Y- and Z-coordinates, before and after intense agitation of the container, were interpreted from the reconstruction of the tomographic X-ray data. The spatial analysis of grains using cell declustering and 3D variography provided a useful means of characterising the distribution of the grains in space and the extent to which they had become segregated during the process of agitation. Spatial analysis indicates that in general, grouping and continuity of grains is weakly developed. The modelled nugget effect remains high for most tests, indicating that grains of similar size did not group together strongly. Agitation through rolling provided the best continuity, although not consistently in the long axis of the plastic jar. The nugget effect was consistently highest, and the range of continuity shortest in samples that had been split, indicating that the grouping and continuity were reduced through splitting the sample. Initial results indicate that very little segregation of the minerals and metals occurs in the quartz substrate. This is somewhat counter-intuitive, but suggests that the effects of agitation on mixtures of dense minerals and metals in substrates of lower density do not lead to excessive segregation. Differences in behaviour between dense materials in response to agitation are evident. Grains of different composition and shape did not group and segregate in a predictable manner in response to the agitation methods. Initial results indicate that the degree to which a material groups and segregates relates more to the characteristics of the denser material itself, rather than the agitation method employed.CITATION:Minnitt, R C A, Jashashvili, T, Gilchrist, G and Dominy, S C, 2017. Quantifying segregation of minerals and metals in particulate materials using computed X-ray tomography and variography, in Proceedings Eighth World Conference on Sampling and Blending , pp 173–182 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By K H. Esbensen

The Theory of Sampling (TOS) has made remarkable strides and has witnessed a suite of achievements over its ~65-year history; especially in the last 15 years, since organised work began for the erstwhile fragmented sampling community (2003). Theory and practice have been very well exposed and disseminated, as witnessed in the successful series of the World Conference on Sampling and Blending (WCSB) events and other dedicated sampling and mining conferences in Australia and South Africa. At this WCSB8 occasion we fittingly devote a section of our conference to celebrate the life and monumental achievement of Pierre Gy, the originator of the Theory of Sampling; a special issue of TOS Forum was recently published to the same purpose. But TOS is most emphatically not a finished work.Here I try to look into the future with a personal view: TOS – what’s next? Commensurate with this objective there is offered some out-of-the-box thinking. The purpose is to try to zoom in on probable additional future aspects of TOS and its application compared to the first 15 years of our common journey. There are bound to be lacunae here-and-there across the vast application horizon delineated, which is as it should be, as this lecture is meant to initiate a broad(er) debate – and readers may agree with all, parts, or none of the topics presented. All is well that furthers this debate.CITATION:Esbensen, K H, 2017. Theory of Sampling – what’s next?, in Proceedings Eighth World Conference on Sampling and Blending , pp 29–38 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By V Rodríguez, M Hormaza, R J. Romañach, C Pinzón-de la Rosa

The authors have presented their ideas for developing a powder sampling device in the I-Corps™ program sponsored by the National Science Foundation (NSF). This is an intensive customer discovery program developed to increase the economic impact of the research sponsored by NSF.The I-Corps™ program requires performing at least 100 interviews with potential customers in various industries. These interviews are essential to establish the need for a particular product or solution, and identify potential commercialisation partners and competition. The interviews have revealed the absence of Theory of Sampling (TOS) principles in many companies. The interviews are designed to identify the difficulties associated with current sampling practices that do not meet TOS principles.The I-Corps™ program is a significant challenge for scientists who often avoid discussing a concept in fear of losing their research idea. The program teaches how to obtain information without releasing the important intellectual property related to a product development idea. The I-Corps™ methods have been used to discover the prevailing ideas on sampling in various industries.I-Corps™ also teaches how to present ideas to investors; another significant challenge for a scientist. We are currently learning to present sampling and TOS to investors and business development experts, and will share some of these experiences.CITATION:Pinzón-de la Rosa, C, Rodríguez, V, Hormaza, M and Romañach, R J, 2017. Theory of Sampling meets the National Science Foundation I-Corps™ program, in Proceedings Eighth World Conference on Sampling and Blending , pp 351–354 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By José Cipriano Vilca Valdivia

El presente texto describe el método Crys-Explo o Exploración Cristaloquímica -en tanto descubre mineralizaciones de estaño en lugares donde otros métodos no tuvieron éxito. El procedimiento ayuda a identificar las zonas con ordenamiento atómicos específicos relacionados con la ubicación espacial de mineralizaciones de interés económico importantes. Asimismo cuantifica la concentración de los elementos químicos en la cristal estructurada de los minerales como óxidos, sulfuros y silicatos y determina el grado de evolución y su función a nivel molecular específica de cada uno de estos Estado Cristalo-Estructural (ECE).

May 1, 2017