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By M G. Millad, M Zammit

The Kipoi Central (KPC) copper project is located in the Katanga Province in the Democratic Republic of the Congo and is presented here as an example of an operating mine where the Mineral Resource was initially estimated by uniform conditioning (UC) and subsequently updated to localised uniform conditioning (LUC). This paper discusses the implementation of an estimate run using LUC and compares the results based on resource definition drilling to those obtained by close spaced grade control drilling and actual mill production.UC is a nonlinear geostatistical technique designed for recoverable resource estimation, typically using wide spaced exploration and resource definition drilling. The primary output of UC is the grade, tonnage and metal above a series of user-defined cut-off grades for a particular selective mining unit (SMU) block size. The UC approach is considered to provide a more accurate representation of the recoverable grade and tonnage at SMU support for non-zero grade cut-offs than would typically be achieved by a traditional linear estimator such as Ordinary Kriging. LUC is a relatively new approach involving the post-processing of a UC estimate and is used to spatially present the grade distribution predicted by UC at the SMU scale.This study demonstrates that the calculation of a more accurate and realistic grade-tonnage relationship for small SMU sized blocks is possible at the exploration stage using LUC, provided that the necessary theoretical and practical conditions for implementation of this technique exist.CITATION:Millad, M G and Zammit, M, 2014. Implementation of localised uniform conditioning for recoverable resource estimation at the Kipoi Copper Project, Democratic Republic of the Congo, in Proceedings Ninth International Mining Geology Conference 2014 , pp 207–214 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 18, 2014

By P F. Hodkiewicz, J de Lacey, M Stewart, R Lane

"Implicit modelling is an approach to spatial modelling in which the distribution of a target variable is described by a unique mathematical function that is derived directly from the underlying data and high-level parametric controls specified by the user. This modelling approach may be applied to discrete variables such as lithology (after converting the discrete codes to numeric values) or to continuous variables such as geochemical grades. This paper discusses the estimation of continuous (grade) variables using implicit modelling.One of the underlying engines of implicit modelling for producing this mathematical function description is the radial basis function (RBF). In essence, the RBF is a weighted sum of functions positioned on each data point. A system of linear equations is solved to derive weights and the coefficients of any underlying drift model coefficients. Once derived, the RBF may be solved for any unsampled point or averaged over any volume to provide an estimate of grade. It is possible, for example, to query the RBF on a regular grid to derive an estimate of block grades. Given the ease of creation of an RBF, and its ability to predict grade, the question arises as to how the grades derived from the solution of an RBF compare with grade estimates derived from conventional geostatistical interpolation methods (eg ordinary kriging (OK)).The purpose of this paper is to describe in lay terms: the basic structure of an RBF the role of parametric choice in the solution of RBFs and how this influences the character of the solution the fundamental similarities and differences between RBFs and conventional geostatistical estimators.Using a high-resolution conditional simulation, we show that in many situations RBF and OK estimates are very similar.CITATION:Stewart, M, de Lacey, J, Hodkiewicz, P F and Lane, R, 2014. Grade estimation from radial basis functions – how does it compare with conventional geostatistical estimation?, in Proceedings Ninth International Mining Geology Conference 2014 , pp 129–140 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Aug 18, 2014

By K Ehrig, C Lower, V Liebezeit, M Smith, E Macmillan

"Minerals, minerals, minerals. Elements occur in the earth as minerals. If local physical, chemical and structural conditions are conducive to concentrating these minerals to sufficiently high abundances in the earth’s crust, a mineral deposit will form. If technology is available to extract metals from the minerals economically, then you have a mineral resource that will be mined and processed. Determining which minerals are present and quantifying their abundances, textural characteristics (grain size and shape) and how they are intergrown with each other is the realm of the mineralogist. Geologists use this mineralogical data to understand how the mineral deposit formed, how to find high-grade zones within the deposit and where to find other mineral deposits. Metallurgists use this data to design, operate and optimise processing facilities to extract metals from the minerals. The types, quantity and precision/resolution of the mineralogical data required by geologists and metallurgists are different, but are not mutually exclusive of each other. Fortunately, measurement technology is available that is relatively rapid, high volume and low cost, thus enabling ore and gangue mineral characterisation at a historically unprecedented scale of a mineral resource.CITATION:Ehrig, K, Liebezeit, V, Smith, M, Macmillan, E and Lower, C, 2014. Geologists and the value chain – how material characterisation by modern mineralogy can optimise design and operation of processing facilities, in Proceedings Ninth International Mining Geology Conference 2014, pp 5–14 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Aug 18, 2014

By R Hargreaves, C Morley

The authors often see problems on mine sites that are due to insufficient feedback between technical discipline silos as the realities on the ground fail to inform the models and plans used to drive resource exploitation. Resource modelling, reserve definition, life-of-mine planning, short-term mine planning and scheduling, mine operations and beneficiation operations are all processes that inform each other, but in many instances feedback between these processes occurs in an ad hoc way and only after problems have come to light.Universal reconciliation uses a multidisciplinary approach across the entire mining value chain to identify both opportunity and loss of value and to maximise operational performance. This paper will look at the operational effectiveness of universal reconciliation and will outline the important elements that make it successful, including how to map out the detail underpinning modelling, planning and execution processes. We do this by documenting the physical processes, logical work practices, organisational roles and responsibilities, data origin and quality, working assumptions and information management practices at an operation. The results quickly show how misinformation and assumption causes suboptimal decisions to be taken, where inefficiencies exist and what the opportunity is to remedy these issues.A universal reconciliation study opens up an opportunity for stakeholders to reassess how the operation should be run and how operational effectiveness should be measured. Clear envelopes for reconciliation become obvious and questions about the practicality and data quality can be addressed.A reconciliation code of practice is a key element of a successful universal reconciliation project and the authors outline the important aspects that will provide an operating model, showing the effectiveness of the resource and reserve modelling, the practicality of the mine planning and scheduling practices, how well the mine operations team are executing these schedules and how effective the processing area is given what it is fed. Most importantly, a system that allows operations time to react before the losses become systemic and that is based on identifying early warnings from hard data when these processes start to wobble is outlined. Finally as a case study, the revenue and cost benefits resulting from universal reconciliation diagnostic projects for nine mine sites is documented.CITATION:Hargreaves, R and Morley, C, 2014. Universal reconciliation – a multidisciplinary approach across the entire mining value chain to identify loss of value and maximise operational performance, in Proceedings Ninth International Mining Geology Conference 2014 , pp 411–420 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 18, 2014

By M Creech

During the 1990s, wider and more productive longwall panels were being introduced into the Newcastle Coalfield. The strata of the Newcastle Coalfield incorporate numerous massive sandstones and conglomerates, some of which formed the immediate roof of the coal seams being mined. The unexpected implications of introducing wider panels into this geological environment included more variable and increased subsidence, severe periodic weighting (with associated mid-face falls) as well as windblast.Subsidence prediction at the time was based on empirically derived data essentially relating subsidence to the width/depth ratio of the panel without accounting for actual widths, nor the impacts of variable geology. This approach required modification as locally derived data was gathered and reinterpreted.The interaction of massive strata located close to the roof of the mined seam resulted in dramatic implications for some of the operations. When associated with longwall goaf geomechanics severe periodic weighting events and crippling mid-face roof falls were the consequence. When associated with narrower panels and shortwall goaf geomechanics, mines in the region experienced aggressive windblast events. The relationships between the overlying massive strata, longwall panel geometry and the various risks encountered during this period is in the author’s opinion, the most concise still documented.As the extensive coalfields of Queensland turn to underground mining operations the relevance of these experiences cannot be underestimated. This paper will discuss recent developments as well as incorporating diverse subjects such as microseismic monitoring, low frequency radio emissions and ‘electrophonics’ as they relate also to the topic.Like Moriarty, massive strata can be disguised and can offend when you least expect. This paper documents a trail of crimes committed by conglomerate in the Newcastle Coalfield that were inflicted on the general public, miners and mining equipment. Although this paper concentrates on the criminal behaviour of conglomerate, this story is also relevant to other massive units such as coarse sandstones that may be more difficult to identify and apprehend. Currently Moriarty remains at large and able to reoffend at any time, once again taking advantage of the unwary.CITATION:Creech, M, 2014. Moriarty was a conglomerate – experiences and implications of coal mining under massive strata in the Newcastle Coalfield, in Proceedings Ninth International Mining Geology Conference 2014 , pp 47–54 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 18, 2014

By T Berryman, C Adams, L Griesel, C Buxton

The Kalgoorlie Consolidated Gold Mines (KCGM) schedule model is used for short- to medium-term planning and scheduling at the Fimiston Open Pit Mine (the Superpit). The model bridges the gap between grade control models, which are generated one to seven days prior to mining, and the annual Resource and Reserve model update that is used for budget and life-of-mine plans. The schedule model uses close-spaced grade control data (up to 20 000 m drilled each month) as well as resource definition drilling to estimate production volumes three to six months ahead of current mining. From this, a rolling 12 week mining and processing schedule is generated on a monthly basis. As the KCGM operation is mill and sulfur constrained, the schedule model needs to be an accurate predictor of ounces as well as tonnes, gold and sulfur grades. The process must also be time efficient in order to meet monthly planning and reporting deadlines. Due to the large amount of new data generated each month, model updates using conventional interpretation and estimation techniques have proven to be impractical and too time consuming. This paper will examine the evolution of the Superpit schedule model process over time, recent improvements and the effect of different model outcomes on short- and medium-term mine planning.CITATION:Griesel, L, Buxton, C, Adams, C and Berryman, T, 2014. The superpit schedule model – A time-efficient data-driven estimation for short- and medium-term planning, in Proceedings Ninth International Mining Geology Conference 2014 , pp 397–410 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 18, 2014

By G Fahey, S Rose

"Mining geology as a defined profession is only about 100 years old. The successful modern mine geologist needs to be a multidisciplinarian, but also needs to maintain a focus on the key outputs of the role. The technical skills needed in the role have increased as mining methods move to increasingly mechanised techniques. As well as being the guardian of the orebody and ensuring minimal dilution to expected ore grades, the mine geologist should be competent in the 12-point mine geology process. The mine geology process should include:a statement of purpose (a ‘mission statement’)geological mapping/logging system that supports consistent and regular geological data collection, storage, and interpretationsampling system that can produce representative samples and fits in with the mining scheduleassay database with relevant controls to ensure only ‘clean’ data is loadedsimple quality assurance / quality control (QA/QC) system for samples and assays, with a response process to react to out of specification resultsconsistent method of exporting, visualising, processing and interpreting assay data to create mining blocksdevelopment sign-off system so that all development and stoping is controlledproduction tracking system to monitoring and reporting what ore and waste volumes have been moved from the face, to any intermediate stockpiles, the run-of-mine (ROM) pad and into the crusherreporting system for daily, weekly and monthly reporting of tonnes and gradesdocumentation so that the grade control system has simple, but effective descriptions of how it works, who has responsibilities, and authorities, and be easily maintainedreconciliation system to compare resource and reserve to grade control and to mined and milledtraining system so that the geology team can quickly become competent.In this paper, the authors discuss the requirements of each point, and comment on common shortfalls observed by them over several years’ experience on producing mines worldwide.CITATION:Rose, S and Fahey, G, 2014. Modern mining geology, in Proceedings Ninth International Mining Geology Conference 2014 , pp 461–466 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Aug 18, 2014

By K H. Esbensen

The Theory of Sampling (TOS) has contributed significantly to chemometrics by allowing data analysts to become more aware of total measurement uncertainty as total sampling error + total analytical error. This has allowed a new view on, for example, validation of multivariate data models and their performance. This keynote focuses mostly on the reverse perspective: how can chemometric multivariate data modelling be of help in an augmented sampling context? The basic principles of multivariate data analysis are presented and application examples are outlined from several areas in the geosciences to facilitate answers to the question posed, including a geochemical multivariate anomaly concept defined by a multivariate background model. Applications from geochemical prospecting, geochemical mapping, environmental chemistry and anthropogenic outlier recognition are outlined, as are the interdependent relationships between geostatistical modelling (variographics) of singular variables versus linear combinations of a multitude of variables, in chemometrics called scores. The potential synergy between chemometrics and TOS is far from exhausted at this time; much work remains.CITATION:Esbensen, K H, 2014. Theory of sampling in a multivariate perspective – what can chemometrics contribute?, in Proceedings Sampling 2014 , pp 7–10 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By P J. Jacobs

FORACO is the third-largest global drilling services company, and recently celebrated its 50th anniversary. The company’s historical roots are in French-speaking West Africa, delivering hydrogeological drilling services to resource companies, villages and regional centres. Today, FORACO operates over 300 drill rigs in more than 20 countries.The Asia Pacific region of FORACO runs a fleet of 37 late model drilling rigs servicing the diamond drilling, reverse circulation, coal, coal seam gas and hydro drilling sectors.FORACO Australia has extensive experience delivering large diameter coring (LDC) samples to the resource community. In the past three years, the company has been involved in six 8? coring projects in a variety of geographies and geologies, including iron ore, coal and uranium. FORACO has developed proprietary equipment and techniques to deliver cost-effective LDC samples with outstanding core recovery.The FORACO LDC sampling system is hands free, with mechanised rod and corebarrel handing to minimise the risk of manual handling injuries. Sample volumes are six times PQ (the common industry standard for large core samples), with core recoveries consistently above 96 per cent in extremely difficult formations.Eight inch coring is a conventional coring system suited to hole depths up to 150 m. For deeper applications, FORACO has recently invested over $4 M in a 6? wireline coring system capable of achieving depths of 300 m.CITATION:Jacobs, P J, 2014. Large diameter core sampling, in Proceedings Sampling 2014 , pp 71–76 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By G J. Halstead, R C. Steinhaus

The True Pipe® in-line sampler was developed in an attempt to address the need for a mechanically reliable and correctly designed sampler to suit one-dimensional pressurised slurry pipelines, often found in metallurgical process plants but for which no reliable and representative sampling solution as yet exists.The design, whose operation is based on the principle of symmetry, is described by Dominique François-Bongarçon (2005) with the synchronous opening and closing of relevant valves. It representatively isolates portions of the particulate material stream at predetermined intervals for effective extraction of primary increments for subsequent sample compositing.Theory of Sampling applicable solutions have proven elusive in flowing pressurised particulate material streams, with various attempts to date not having formulated a viable, mechanically reliable and acceptable design.Compliance to sampling theory is critical, and a number of tests were conducted where certain operating parameters were altered to determine their influence and sensitivity on sampling representativeness.The results of the initial series of tests performed on a carefully designed rig are presented, with additional work discussed for necessary investigation in the near future.Getting closer to a robust, economic and correct design for this challenging application in the minerals industry may now not be that far away. Plant management staff will see improved metallurgical mineral balances around their process plants and significantly reduced sampling discrepancies, particularly in tailings applications. Improved quality control is now possible where, due to plant head room constraints, classical cross stream sampling methods cannot be employed.CITATION:Steinhaus, R C and Halstead, G J, 2014. True pipe sampler – a correct design utilising the principle of symmetry in pressurised slurry particulate streams, in Proceedings Sampling 2014 , pp 127–134 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By J E. Powalisz

"The application of automated sampling techniques can help to provide a representative sample of process fluids and slurries that provide data for controlling process, custody transfer and quality assurance and quality control purposes.The automation of sampling provides a more consistent and reliable sampling process and eliminates many of the human-induced errors. Automated sampling can also provide a higher degree of safety in the plant/processing environment as well as better bottom-line results.Automated, fixed volume samplers can provide a very representative sample by integrating many small ‘grabs’ of sample from a process. Automated samplers can be controlled in a variety of ways to optimise the sampling process, including:cycle ratedwell timetotal volume.Properly selected and applied, an automated sampler can effectively isolate the operator from the process fluid during sampling and, to a great degree, during sample container collection.A variety of materials of construction, including seals, are available to handle demanding mining applications. Alloy 625, alloy 2205, AL6XN® and even certain plastics are used successfully in mining slurry applications.This paper explores the application of automated, fixed volume, batch style samplers in copper/nickel and gold processing, specifically pregnant leach solution and extraction fluids.Several specific field applications are discussed, including process installation details, hardware and operation.CITATION:Powalisz, J E, 2014. The application of automated samplers in copper/nickel processing, in Proceedings Sampling 2014, pp 35–42 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jul 29, 2014

By P Minkkinen, J Lohilahti, T Korpela, B McPherson

Concentrator plants intake the ore from the mine and produce concentrates by extracting valuable minerals in a succession of process steps. Controlling the extraction process and detecting changes in the process requires updated information on a minute basis. Moreover, due to economical reasons, one must also collect metallurgical samples on a daily basis for monthly reporting.In this paper, we present how slurry samplers were installed in a Cu-Ni flotation plant to accomplish the aforementioned tasks. An on-stream elemental analyser at the plant is used for flotation process control by taking samples from selected process streams. Two process streams, the flotation feed and tail, are also equipped with metallurgical samplers. The metallurgical samplers were designed to have low head loss, be robust and easy to maintain and to collect slurry samples in a reliable way for the metallurgical accounting.Samples were collected by different sampler types and we discuss the uncertainty arising from sampling in the assays. Variographic analysis was carried out on the process data in order to find periodic components in the process, and estimate the correct number of shift sample increments and measurement error components. The metallurgical samplers were proven to be reliable devices with zero downtime and no sanding issues in the main process flows. In addition, we show that the tail measurement is the most important one for the plant recovery calculation, which emphasises the importance of the sampling accuracy in the tail stream.CITATION:Lohilahti, J, Korpela, T, Minkkinen, P and McPherson, B, 2014. Analysis of slurry sampling arrangements at a froth flotation plant, in Proceedings Sampling 2014 , pp 91–98 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By L Lachance, F Flament, D Leroux, S Gariépy

The pervasiveness of poor sampling practices in and around mineral processing plants could seem intriguing in view of the various documented evidences both for causes and consequences. To this extent, this paper revisits the well-documented but lesser-known fact that failure to care sufficiently about how samples are selected and managed is the mother source of almost all sampling biases. The authors believe that the potentially huge leverage between causes and consequences as well as the diversity of people involved in sampling call for a globally integrated solution rather than for a point solution. Such a globally integrated solution has already been put forward by the AMIRA P754 Metal accounting – code of practice and guidelines (AMIRA, 2007) that inherently requires accuracy to be targeted, measured, reported and managed. Furthermore, always in order to avoid a point solution, several layers of protection against biases must be put in place with each one involving a different ownership whenever possible. This is exactly the role intended by the supervisory bias detection method described in this publication. It is based on the continuous monitoring of the expected value of reduced residuals obtained from statistical data reconciliation conducted in a metal accounting context. This supervisory bias detection method is developed in full details after which Monte Carlo simulations are done on a mineral processing plant example for showing how it can be successfully applied for detecting hidden biases.CITATION:Lachance, L, Leroux, D, Gariépy, S and Flament, F, 2014. Detecting sampling biases in metal accounting, in Proceedings Sampling 2014 , pp 109–120 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By F F. Pitard

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

Jul 29, 2014

By D W. Stevens

Mechanical sampling provides a definitive value for the product sampled but the results are not instantaneous, meaning that decisions on process control can only be made hours after the sample is taken. Chemicals are added, tailings are sent to waste, stockyards are laid and the data for the decision-making is historical. Online analysers provide instantaneous information, allowing operators to make immediate and cost-saving decisions on the process. However, pending the specific online analysis application, there is a need for reference sampling for dynamic calibration/bias updating of the analysers so that they can deliver the desired precision and accuracy. What would be the economic justification for combining mechanical sampling with online analysis to reduce laboratory costs and provide immediate process control with certainty of accuracy? Approved sampling according to the Theory of Sampling, with immediate process control information.CITATION:Stevens, D W, 2014. Justification for integrating mechanical sampling with online analysis, in Proceedings Sampling 2014 , pp 135–138 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By M Graham, R Brunning, C Andringa-Bate, S Westergren

In 2010, Rio Tinto Iron Ore’s operations commenced the planning and design of a sampling and analysis system for the new 50 Mt/a Cape Lambert Port B (CLB) facility. Current sampling systems at the ports consist of automated robotic, mass-based sampling protocols employed at Dampier, and semi-automated, time-based sampling systems operating at Cape Lambert Port A.The new sampling system at CLB has brought together a fully automated sampling and analysis system utilising time-based sampling, with the capacity to sample ore products of medium quality variation as defined in ISO 3082 (ISO, 2009). The CLB system is required to sample at shiploading rates from 6000 t/h to 13 440 t/h and provide full load-port analysis. The sampling approach is to proportionally extend primary increments (of varying mass relative to shiploading rate) to a standard length using variable speed aero-belt conveyor systems. This enables a fixed number of secondary cuts to be achieved for all primary increment masses and to be delivered to the automated cell. The cell is required to be capable of concurrently processing four individual shipments, each containing multiple cargoes.Achieving the target sampling precision for all export cargo sizes has culminated in the planning, design and construction of the largest automated iron ore port laboratory in the world. The CLB sampling and analysis cell was commissioned in Q3 and Q4 2013. This paper discusses the sampling philosophy and commissioning outcomes.CITATION:Brunning, R, Andringa-Bate, C, Graham, M and Westergren, S, 2014. Cape Lambert Port B shiploading, sampling and analysis, in Proceedings Sampling 2014 , pp 79–90 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By W P. Breytenbach

"The phased application of statistical process control (SPC) infers that processes are classifiable as pre-SPC (or phase zero), phase I and phase II processes, with different objectives being pursued in the different phases. Phase zero processes, defined as processes affected by assignable causes of variation, are characterised as follows: out-of-control with unknown autocorrelation structure having an unknown statistical distribution not performing consistently on target with minimum variability potentially incapable of conforming to the product specifications. The objectives of phase I SPC is to obtain estimates of the mean (µ), variance (s2), probability density function (f(X)) and the autocorrelation structure, as well as transforming the process into a phase II process (defined as a process conforming to specifications and performing on target with minimum variability). As in any other type of research or pragmatic improvement intervention, sampling is required is to achieve these objectives. The challenge of deciding a phase I sampling plan is multifarious since it has to cope with the aforementioned effects of assignable causes and provide for rigour estimates of the unknown parameters. Furthermore, deciding sampling plans in phase I invariably presents a classical catch-22 situation: appropriate sampling is required to determine the autocorrelation structure of a variable, but knowledge of the autocorrelation structure is required to formulate an appropriate sampling plan. This paper suggests, for random variables, guidelines to formulate sampling plans, and presents a methodology to estimate the inherent process parameters of process variables and an approach to identify assignable causes in phase I. CITATION:Breytenbach, W P, 2014. Rational sampling and standard deviations for random variables in phase i application of statistical process control, in Proceedings Sampling 2014, pp 141–154 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jul 29, 2014

By G M. Jeffress, D C. Arne

There are two discrete aspects to collecting portable X-ray fluorescence (pXRF) data to obtain reliable results and to comply with reporting requirements under the 2012 edition of the JORC Code (JORC, 2012) – sampling method and quality assurance. A robust sampling program is required to obtain representative material for analysis. The appropriate method should be established for a particular project using a small orientation study. A variety of approaches to sampling soils, drill cuttings, rock faces and diamond drill core are discussed. These include using multiple spot analyses, scanning using a single operating beam and the collection of cutting saw pulps. The operator must also be aware of the attenuating effects of analysing through sample bags of different material types for various elements as well as the limitations of current pXRF instruments in reporting reliable data for certain elements. The analysis of the samples must also be accompanied by a quality assurance program in order to generate data that are ‘fit for purpose’. This includes recording X-ray tube resolution on a routine basis, using matrix-appropriate standards for which total elemental concentrations are available, using silica blanks, capturing sufficient duplicate analyses to constrain data precision and obtaining check assays to calibrate pXRF data against laboratory data. Examples of quality control approaches and data are presented. Table 1 of the 2012 JORC Code also recommends that information on instrument make and model, reading times and calibration be provided in any reporting of pXRF data. To this should be added information on operating mode and temperature, software version and moisture content of the samples. A robust sampling methodology with a suitable quality assurance/quality control program should produce pXRF data of sufficient quality for public reporting purposes, provided that the data are presented using appropriate cautionary language and adequate supporting information.CITATION:Arne, D C and Jeffress, G M, 2014. Sampling and analysis for public reporting of portable x-ray fluorescence data under the 2012 edition of the JORC Code, in Proceedings Sampling 2014 , pp 57–64 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By R Shaw, T T. Neidel, C Adams

Sampling equipment manufacturers endeavour to ensure that the mechanical equipment installed in sampling systems are designed and manufactured to adhere to the requirements of the International Organization for Standardization (ISO) or other standards relevant to the application. The mechanical parameters necessary to achieve these standards can be well controlled and requirements such as constant speed can be met through good mechanical design and engineering. However, when the equipment is installed and put into operation, the ISO compliance of the system is out of the hands of the equipment manufacturer and handed to the operator, who may be a generalist control system integrator.Making a sampling system perform according to standards, and being able to monitor and report on compliance require that the sampling system knowledge and the control integration is combined and applied in an integrated approach. Using a dedicated library of control routines developed for sampling can change a system from having compliant equipment to being a compliant system. The library has to include monitoring of relevant parameters and control and reporting on sampling parameters.CITATION:Neidel, T T, Adams, C and Shaw, R, 2014. Control and monitoring of International Organization for Standardization compliance for industrial sampling systems, in Proceedings Sampling 2014 , pp 121–126 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By K M. P Singh, K M. K Sinha, G S. Jha, T Gouricharan, K K. Sharma

The majority of the powerplants in India are fed with non-coking coals, with the coals dispatched from their respective mines via wagons loaded by a rapid loading system or pay loader. The usual top size of coal is 200 mm and sampling is carried out manually from wagons. The manual method of wagon top sampling of large size raw coal is not only difficult, but also violates some of the fundamental principles of sampling. As per sampling requirements, samples are to be drawn from the full depth of the wagons, which is impossible to be done manually. Since the ash distribution in the different size fractions is not homogeneous, the results from the samples, which do not reflect the true size distribution of the lot, are likely to be biased. More importantly, sample collection by shovel from the top is a function of human discretion and not governed by the equiprobability rule. In another case, the sample has been collected by a cross belt-type auto mechanical sampler for 200 mm size coal at one of the super thermal power stations. Test analysis shows that coal collected using a cross belt-type mechanical sampler also has significant deviations from stopped belt coal samples of 200 mm size due to size segregation. Studies carried out on wagon top sampling through specially designed experiments reveal some interesting features. The presence of bias in wagon top sampling is apparent when compared with the ‘stopped belt’ sample. In this paper, a comparative study between manual wagon top sampling/auto mechanical sampling with respect to stopped belt sampling has been conducted for coal of size 200 mm. In addition, precision studies have been performed for wagon top sampling/mechanical sampling for Indian thermal powerplants.CITATION:Sinha, K M K, Sharma, K K, Jha, G S, Singh, K M P and Gouricharan, T, 2014. A comparative study of manual wagon top sampling and auto mechanical sampling of 200 mm size coal with respect to stopped belt sampling of thermal coal at Indian super thermal powerplants, in Proceedings Sampling 2014 , pp 43–48 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014