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By K H. Esbensen, A Arkenbout

In the north of the Netherlands, in the environment of Harlingen, an industrial waste incinerator was installed in 2011. The population in the region is concerned about adverse health effects related to possible emission of dioxins. This study compares the relative merit and reliability of four sampling approaches aimed at quantifying the possible pollution patterns surrounding the incinerator: short-term versus long-term isokinetic flue gas sampling, spatial sampling of grasses and a novel approach developed by the Toxicowatch Foundation, based on local composite sampling of ten eggs from backyard chickens (local area pollution load averaging by accumulating bio-entities). Based on the latter approach results of a new analytical bioassay, DR CALUX®, from 15 locations near Harlingen harbour show distinct above-threshold dioxin levels in the environs of the incinerator. Currently only short-term flue gas sampling is mandated by the authorities; based hereupon, under normal operating conditions, the incinerator appears to be compliant with emission standards. This short-term sampling scheme is seriously flawed, however, in that it only demands one continuous 12-hour sampling period per annum – an extreme grab sampling transgression in the time domain. In starkest possible contrast, significantly elevated dioxins emissions were measured in flue gas during events of unstable combustion conditions by continuous long-term measurements. The dioxin congener patterns from long-term flue gas sampling show similar patterns as the congeners found in backyard chicken eggs and grass, evidence that elevated dioxins in eggs is due to emissions from the incinerator. These results make it mandatory to perform long-term, continuous measurements for all sources where similar high-temperature combustion/emission processes take place. Backyard chicken eggs are a prevalent regional bio-accumulating sample type in rural and agricultural regions, which is relevant to air pollution emission monitoring. There are interesting relationships between this type of sampling bio-sensors and the special bioassay DR CALUX® features, which, although quite outside traditional contexts, never-the-less all can be well understood in the context of the Theory of Sampling. We venture to unravel the complex interactions between correct and incorrect sampling errors and the extraordinary complicated analytical issues involved in monitoring and source tracking using accumulating bio-sensors. The present contribution is but a first foray indicating the general total sample error scope and total analytical error framework needed for this approach.CITATION:Arkenbout, A and Esbensen, K H, 2017. Sampling, monitoring and source tracking of dioxins in the environment of an incinerator in the Netherlands, in Proceedings Eighth World Conference on Sampling and Blending , pp 117–124 (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 S C. Labram, A Stabile

"Modern train load out stations are becoming faster and have the ability to load a 100 wagon train in less than three hours. At 70 metric tonnes per wagon, that is an average fill rate of 2333 t/h and considering the train is continuously moving, the instantaneous feed rate into the wagon is in the region of 4200 t/h.The main feed conveyor that maintains the level in the surge hopper, above the final weigh hopper, operates in excess of 5000 t/h to ensure uninterrupted charging of the weigh hopper to not disrupt the train loading operation.To avoid demurrage costs (a charge by the Rail Operating Company for exceeding the contractually agreed loading time) the train has to be handled speedily and efficiently and spend the absolute minimum time at the loading facility. Everything else has a much lower priority, including sampling.Thermo Fisher Scientific’s brief was an engineering, procurement and construction management (EPCM) contract to provide a sampling scheme adjacent to the prepurchased and predesigned load-out facility, but which interfaced seamlessly with the loading system of the trains and provided the following: an instantaneous particle size distribution (PSD) result for each truck loaded, and a final PSD for the total train prior to the train leaving the loading facility a crushed (-3 mm) composite sample for chemical analysis for the whole train a composite physical analysis sample for the whole train.The design presented some interesting challenges to our team, but the end result is a system that not only ‘stands alone’ but interfaces seamlessly with the loading system. This paper discusses the design itself and potential improvements to future systems.CITATION:Labram, S C and Stabile, A, 2017. An automatic sampling methodology to determine contractual particle size distribution in rapid train load out stations, in Proceedings Eighth World Conference on Sampling and Blending, pp 229–234 (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 F F. Pitard, K J. Ehrig

The Olympic Dam iron-oxide copper-gold (IOCG) uranium-silver deposit is second in size only to Norilsk, the gigantic metalliferous deposit. Olympic Dam is the largest publicly declared uranium, fifth largest copper and third largest gold resource. The deposit is typified by texturally destructive brecciation and intense iron metasomatism of the Roxby Downs Granite, bedded clastic facies rocks and mafic-ultramafic dykes. The deposit contains over 100 minerals. Copper mineralisation consists of chalcopyrite, bornite and chalcocite. Uraninite, coffinite and brannerite are the dominant uranium-bearing minerals. Gold mineralisation consists of electrum, tellurides and Au-Cu alloys. Hessite is the major silver-bearing mineral. The economic minerals are disseminated throughout the breccia matrix where the average grain sizes of the Cu-sulfide, uranium, gold and silver minerals are ~50, 20, <5, <5 microns, respectively.A significant number of sampling theory-based test programs have been completed since the mid-1990s to establish sampling nomographs, select appropriate sampling (ie split versus chip and interval lengths) and sample preparation protocols, quantify the errors associated with each subsampling stage, determine the practical uncertainty limits of Olympic Dam matrix-matched standards, and identify improvement opportunities related to the ongoing assaying quality assurance / quality control (QA/QC) program. Invaluable knowledge and unexpected insights gained from sampling studies at Olympic Dam have helped to improve our understanding of the distribution of economic and deleterious elements within the Olympic Dam deposit.CITATION:Ehrig, K J and Pitard, F F, 2017. Underground bulk sampling, uniform conditioning and conditional simulation – unrealistic expectations?, in Proceedings Eighth World Conference on Sampling and Blending , pp 21–28 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By G J. Lyman

In sampling a process stream, conventional sampling theory deals only with the variation of the analyte content and neglects the impact of the variation in the mass flow on the sampling variance, whether the sampling regime is time-based or mass-based. This is an obvious simplification of the real state of affairs and it is worthwhile to study this impact via simulation.With a facility to simulate large (long) random functions having some thousands of points in the function and the ability to impose covariance functions on the mass flow and analyte variations, such a study is possible. It is also possible to evaluate the impact of correlation between analyte content and mass flows.This paper describes a limited study of the variance increase due to mass flow fluctuations over the usual sampling variance due to analyte variation during the sampling period. The bias in the sampling result is determined for specific modes of correlation. Simulation methods are used as they are the only means by which a sampling result can be compared to the true assay for the lot; in the physical circumstances of mineral process analysis, it is never possible to determine the true process stream assay.CITATION:Lyman, G J, 2017. The impact of the weighting error, in Proceedings Eighth World Conference on Sampling and Blending , pp 311–318 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By M Haest, O Dominguez, D Mittrup

Technological innovations and emerging analytical capabilities are transforming the way drill samples are analysed, with real-time mineralogical analysis made possible. In addition, new business/technical requirements are challenging long standing processes to incorporate more sources of information during planning in order to improve compliance to plan during mining and processing. Unfortunately, the new sources of information that are added do not always have adequate protocols in place to ensure quality data is collected.Spectral data covering the visible – near – short wave – midwave – longwave range are routinely collected by the Geoscience and Exploration department at BHP Billiton Iron Ore for accurate mineralogical identification. These data are used for geological interpretations (logging and modelling) and prediction of physical variables (metallurgical and geotechnical properties).This paper introduces how sampling and quality assurance / quality control concepts have been applied in the spectral data process. The described routine for sampling, data collection/management and reconciliation has been developed to track system precision and accurately and consistently ensure the data is of high quality before it is used across the mining chain.CITATION:Mittrup, D, Dominguez, O and Haest, M, 2017. A new approach to implement quality assurance and quality control to technological innovations – quality of spectral data capture and processes in the minerals industry, in Proceedings Eighth World Conference on Sampling and Blending , pp 265–270 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By C J. Kruger, E le Roux

Anglo American Platinum has adopted a strategy of Best Practice Principles (BPP) for sampling, mass measurement, analysis and metal accounting for more than a decade now. When new plants are designed, the most suitable sampling equipment and sampling protocols are implemented and material flows and plant layouts are designed around sampling strategies. A few years ago, a crushing plant for a high-grade matte stream was commissioned.The design called for the high-grade material to be crushed to 95 per cent passing 2 mm and the design allowed for a single cutter primary Vezin sampler, increments taken from a gravity flowing stream at regular intervals, whilst filling a tanker for the pneumatic offload at our base metal refinery. This was a single point of sampling and analysis that would measure the wanted output from our smelting complex and the input to our refineries. In addition, the design incorporated the functionality to collect increments in an ‘interleaved’ fashion from the Vezin sampler for internal precision checks.The metal accounting strategy for Anglo Platinum is the ongoing measurement of all inputs, outputs and stock levels for all our operations for platinum group metal (PGM) and base metal (BM) content. Included are yearly stocktakes, usually done on 1 February every year, across the smelters and refineries (at least the base metal refinery). A trend was developing in the accounting period immediately following the introduction of the new crushing and sampling facility, whereby the absolute difference between the physical stock and theoretical stock (PGMs predominantly) increased for the two respective sites on an ongoing basis. The smelter (sender) showed an increase in physical stock compared to the theoretical stock and the base metal refinery (receiver) showed that their theoretical stock increased against their physical stock. Because of this anomaly, an additional stocktake was held in September for the period in question and it was confirmed through the physical stock measurement results, that the PGM accounting problem was indeed pointing towards the evaluation of the high-grade matte stream.This paper showcases parts of the process that took place to explain the poor accountabilities for the respective sites. Previous results from the sampler ratification process (of the interleaved sampling design) were analysed again, a physical inspection was carried out on the sampling system again and the sampler performance was monitored (quality control parameters) for the period.From these investigations, it was found that airflows present inside the Vezin introduced the sampling bias and a change in sampling protocol was introduced as well as physical changes to the sampling equipment were made to eliminate the airflow and thus the bias conditions significantly. The accounting period which followed, after a phased upgrading process, showed that the accounting between our smelter and base metal refinery had been restored to within statistical limits (difference between physical and theoretical stock below five per cent relative to theoretical stock).CITATION:Kruger, C J and le Roux, E, 2017. Pitfalls in Vezin sampling for finely crushed materials, in Proceedings Eighth World Conference on Sampling and Blending , pp 151–158 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By P Goblet, S A. Séguret, E Cordier, A Galli

In salar-type deposits, lithium grades can be measured by bailers introduced at different elevations in the drill holes at locations where, later, the brine will be pumped out to recover the metal. When they are duplicated, these a priori rudimentary measurements may show large differences without indicating whether this is due to the procedure itself or to some physical causes linked to the dynamic behaviour of the salar (seasonality, rainfall, underground flow etc). In the reservoir presented in the paper, the problem is complicated by the double lack of stationarity of the lithium grades: the grades not only increase importantly with the depth but at the same time their fluctuations decrease, making it necessary to use non-stationary geostatistical techniques to simulate them. Reservoir simulation is developed in two steps: first, geostatistical simulations of the lithium grade create possible realisations of the reservoir; then, each geostatistical simulation is followed by a pumping simulation that calculates, at each drill hole of a given extraction scenario, the lithium produced each year over 40 years. For the set of 100 simulations produced in this way, a scenario reduction is made to choose the five most representative simulations where more complex calculations will be carried out (for example, changing hydrogeological parameters such as porosity and permeability, or the elevation of the filters, the number and the location of the pumping and the reinjection drill holes etc). The paper first presents an analysis of the geostatistical model sensitivity to the grade uncertainty when measured by bailer. Among the 100 measurements at our disposal covering the future production domain, 50 per cent are duplicates measured at the same place but at different times. They are randomly sampled to produce five subsets of 75 values, which will constitute the future 3D conditioning points for the geostatistical analysis and the simulations. For each subset, trend, standard deviation and variogram models are fitted, leading to five sets of 20 geostatistical simulations of grades. In this way, the reliability of the different parameters involved in the procedure is evaluated, as well as its impact on the pumping results. Then, a reconciliation study is conducted between the non-stationary nugget effects encountered in the 3D variograms of the lithium grade and a pure statistical analysis of 25 duplicates measured at different locations and/or different times. The result is that if the nugget effect reaches 20 per cent, which is the case of three conditioning subsets out of five, the reconciliation is good and the nugget effect of the variogram model represents the bailer uncertainty, ie the measurement error. Since the geostatistical simulation incorporates a nugget effect, it handles the bailer uncertainty and the impact on the produced metal is included in the evaluation. The final conclusion is that, after the hydrogeological pumping simulations, the initial differences between the five geostatistical models do not influence the final results: lithium grades measured by bailers can lead to a robust evaluation of the extractable resources, a conclusion that conflicts with conventional wisdom.CITATION:Séguret, S A, Goblet, P, Cordier, E and Galli, A, 2017. Bailer uncertainty evaluation in a lithium salar deposit, in Proceedings Eighth World Conference on Sampling and Blending , pp 395–402 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By G J. Lyman, S A. Tittlemier

International regulations exist to manage the concentrations of mycotoxins such as Ochratoxin A (OTA) and deoxynivalenol (DON) in grains and prevent health risks from excessive exposure to OTA and DON. Since the permitted levels of these mycotoxins are very low (existing maximum levels are in the range of 5 ppb for OTA and 1–2 ppm for DON), sampling for these mycotoxins is potentially challenging. Given that the distribution of infected kernels in a consignment of wheat can be highly heterogeneous, detection by current sampling methods is subject to a high variance. Furthermore, the concentrations of mycotoxins in individual kernels vary over orders of magnitude, intensifying the heterogeneity of the bulk grain.In these circumstances, there is much interest in developing sampling plans that inform regulatory agencies, and mitigate the risk of misclassifying grain shipments as compliant or non-compliant within the global grain trade, when correctly implemented.Experimental data from laboratory and field studies describing individual wheat kernel concentrations of OTA and DON were combined with experimental data describing the variance due to the analytical methods used for OTA and DON to calculate the sampling distribution for wheat having various levels of contamination. This work has focused on quantifying the heterogeneity of samples at the point of submission for analysis and within subsequent sample preparation to arrive at total sampling distributions that include uncertainties due to the intrinsic heterogeneity and the analytical uncertainties. The sampling uncertainty that relates to the difference between the primary sample analysis and the true lot analysis is not included as it has been dealt with elsewhere.CITATION:Lyman, G J and Tittlemier, S A, 2017. The sampling characteristics of grains contaminated by mycotoxins, in Proceedings Eighth World Conference on Sampling and Blending , pp 281–294 (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 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