Search Documents

By I Treloar, T Elenbaas, E Isaaks

Critical reviews of grade control generally focus on blasthole sampling and the estimation of ore control block model (OCM) grades, with little or no attention given to dig-line design. For example, the estimation of block grades is deemed important as estimation errors lead to the misclassification of ore types and subsequent dollar loss. Recognition of the problem that the underestimation of block grades may be more or less costly than overestimation led to the application of loss functions to the classification of individual OCM blocks by ore type. The method is known as maximum profit selection (MPS). However, each OCM block may be misclassified a second time by dig-line design. Although each block may initially be assigned an optimum ore type by MPS, OCM blocks are seldom individually mineable by ore type. As a result, a common solution to this problem is the design of mineable polygons or dig lines loosely constrained by a minimum mining width. All material within a properly designed dig-line polygon can be mined and sent to a single destination such as the mill, stockpile or waste dump. These dig lines are often designed manually or by simple variations of computer-generated contour lines. They are generally suboptimal and result in additional misclassifications of ore types. This paper provides an algorithm for constrained optimum dig-line design where dig-line misclassifications are also evaluated through loss functions or MPS. Constrained optimum dig-line designs minimise the dollars lost by dig line misclassifications for a given minimum mining width constraint. A case study is provided illustrating constrained optimum dig-line design and subsequent benefits.CITATION:Isaaks, E, Treloar, I and Elenbaas, T, 2014. Optimum dig lines for open pit grade control, in Proceedings Ninth International Mining Geology Conference 2014 , pp 425–432 (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 R Sterk, B Reid

The Konongo Gold Project is located on the western margin of the 125 Moz Ashanti Gold Belt in Ghana, West Africa. It is situated 60 km along strike of the world-class mineral system at Obuasi and is interpreted to lie on the same controlling regional shear system.The project is operated by Signature Metals, a subsidiary of the Liongold Corporation, and is managed and funded by the Liongold Corporation.Historic underground operations at Konongo produced 1.6 Moz Au at a head grade of 11.8 g/t Au, from eight operations spread along 16 km of the shear system. Mining focused almost exclusively on free-milling gold in quartz, ignoring the gold-bearing sulfide mineralisation associated with the quartz veins. Underground mining ceased in 1986.In 2013, Signature Metals re-focused the program at Konongo to assess the viability of mining the sulfide ore sustainably. This led to the first campaign review of the historic underground data and resource potential since mine closure. The Obenemase mine was the first of the eight operations to be assessed as part of this campaign.Large amounts of legacy data have survived for Obenemase and several historic resource estimations have been completed based on these data. The data typically consisted of hand-drawn sections and plans, and ledgers of commonly incomplete drilling and sampling data. Some drill core have been lost over time, but a large amount of quarter-core and half-core as well as pulps and rejects are still available. Previous resource estimates had a low confidence value because all the available assay and geological data were integrated effectively with the estimates, resulting in a lack of understanding of both grade and geological continuity.This project built a new geological model for Obenemase that greatly enhances the understanding of the controls on mineralisation using a thorough approach of re-surveying, re-cataloguing and re-logging. This model led to an increased degree of confidence and a subsequent upgrade of resource classification, and created several previously unrecognised strong exploration targets. The resulting resource estimates increased the value of the project by 100 per cent.This paper emphasises and highlights the importance of a well-understood geological model on the confidence of a resource estimate and how this may directly impact on the project’s ‘bottom line’.CITATION:Sterk, R and Reid, B, 2014. The Konongo Gold Project, Ghana – an example of how geology makes all the difference to a resource estimate, in Proceedings Ninth International Mining Geology Conference 2014 , pp 467–476 (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 J Ta’ala, M Handyside

Mechanical sampling from conveyors has historically been a staple in the iron ore industry when information related to chemistry, sizing and moisture is required. In order to comply with ISO 3082:2009, samples can be taken either on a mass or time basis provided no bias is introduced by periodical variation in quality or quantity. With the introduction of mechanical sample carousels, you also have the opportunity to collect samples on a mass or time basis. In order to optimise automatic sample stations in a changing production environment, BHP Billiton Iron Ore (BHPBIO) has developed a simulation model that enables the various combinations of mass–mass, mass–time, time–time and time–mass to be investigated.The sample station model can utilise actual (variable) feed rates to achieve high resolution and enables the simulation of sample size variation. Cutter speeds and other settings can then be adjusted to better optimise the performance of the sample station with regards to optimal sample size as well as ensuring the sample station is in compliance with ISO 3082. The model can also assist process engineers in adjusting settings to better optimise the sample station and reduce unwanted events such as blockages. The model can be easily modified to conform to various sample station designs. This paper demonstrates the use of the model and the benefits that can be achieved.CITATION:Handyside, M and Ta’ala, J, 2014. Simulating time versus mass basis?sampling, in Proceedings Sampling 2014 , pp 155–164 (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 D Laskovski, T Nguyen

Flotation surveying can be an extremely powerful means of determining opportunities for circuit optimisation and benchmarking. The type of survey employed depends heavily on the nature of the information required from the survey. It follows then that the type of survey determines the type of samples required; however, regardless of the type, the success or failure of a survey is often dependent on the correct used of appropriate sampling techniques at each sampling point. For a successful flotation plant survey it is vital that the correct samples be collected at the correct time in respect to the feed and each other. This paper discusses the current best practice sampling methods used in flotation surveying and discusses in detail the ideal sample masses based on theoretical analysis. Comparisons with JKTech’s database of real plant operations will then be made.CITATION:Laskovski, D and Nguyen, T, 2014. An investigation of current flotation survey sampling, in Proceedings Sampling 2014 , pp 99–106 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By D François-Bongarçon

The theory of sampling (TOS), and therefore representative sampling, is based on three fundamental principles: sample correctness, which handles the search for non-bias; segregation, the effects of which must be eliminated or minimised; and variance control to help achieve the desired reproducibility. After briefly reviewing these very few, albeit essential first principles of Gy’s TOS, the state-of-the-art in TOS is examined through the prism of these principles and the prospects for the future are explored, with suggestions for further research where relevant. Regarding segregation, which is currently the focus of much research, new orientations are proposed. For correctness, which takes on a major importance when it comes to sampling devices, some new, important principles that have been put forth by recent research, are described in details.CITATION:François-Bongarçon, D, 2014. State of affairs and current research trends in theory of sampling fundamentals, including segregation and correctness, in Proceedings Sampling 2014 , pp 11–16 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 29, 2014

By J Webb

"It is vitally important in projects where the cut-off grade is low that there is no contamination during the sampling and sample preparation process, both in the ore and the waste zones. Failure to identify contamination could have devastating financial consequences for a project. The use of a variety of blank types is essential for ensuring that contamination is identified and eliminated at every stage of the sampling and sample preparation process. This paper examines an instance where an effective assay quality assurance and quality control (QA/QC) program was able to identify and eliminate a source of significant multi-element contamination in the prefeasibility stages of an open pit project. This examination includes the: evidence for contamination findings of the investigation process of resolving the problem and correcting the data evidence for both addition of metal and dilution through contamination potential impact on the project had this issue not been dealt with. It was through the use of a combination of blank types that the QA/QC process at Mount Isa Mines (MIM) identified a contamination issue at the first crushing phase of sample preparation. Identification of the contamination allowed MIM to stop assaying activities and work with the laboratory to correct the problem. The sampling program could then be continued with no further doubts about the sample preparation process. Contaminated samples were resampled from retained core. With the continuous monitoring provided by the blanks program, it was possible to ensure that only clean results went into the resource modelling data set. CITATION:Webb, J, 2014. When nothing is what you need – the importance of using blanks at all stages of sample preparation, in Proceedings Sampling 2014 , pp 49–54 (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 S A. Campbell

There has been a reduction of coal production in the United States due to environmental pressure and the increased use of natural gas. This reduction will require coal producers and consumers to make decisions on whether to sample more to verify contract quality or sample less in an effort to cut costs.CITATION:Campbell, S A, 2014. The shrinking coal market in the US – sample more or sample less?, in Proceedings Sampling 2014 , pp 3–6 (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 R Minnitt

Two methods of calibrating the parameters K and Alpha for use in Gy’s formula for the fundamental sampling error were investigated. Approximately 480 kg of broken ore from Harmony’s Target gold mine in South Africa was used to compile the replicate samples for two experiments. The duplicate series analysis (DSA) and segregation free analysis (SFA) methods differ in terms of their approach to recovering the sized materials required for the calibration procedure. In this experiment the DSA method split off six more-or-less equal masses of material from a 75 kg lot that was successively crushed to finer and finer fragment sizes. The sublots so formed were split into 32 samples and fire assayed for gold. The SFA method used approximately 250 kg of broken ore crushed to 95 per cent passing 2.50 cm, which was then passed through successively finer screens until a sublot of less than 0.0075 cm was obtained. Each of the 15 sublots obtained in this way were split using a riffle splitter into 32 samples that were fire assayed. A total of 672 fire assays were used in these experiments. The sampling parameters K and Alpha were used for the compilation of a nomogram and to calculate the gold liberation size of the ores. While there are no definitive results in terms of the actual gold grain sizes against which the calculated sizes can be compared, it appears that the DSA method produces more acceptable results.CITATION:Minnitt, R, 2014. A comparison of two methods for calculating the constants K and alpha in a broken ore, in Proceedings Sampling 2014 , pp 165–178 (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 J E. Powalisz

Obtaining a representative sample of bulk materials in process industries is critical to providing valid data for process control and custody transfer (valuation). The accuracy of a sample, that is to say how well the sample represents the material in the process, is directly related to how well the process can be controlled and to how much a product may be worth. Sampling accuracy then is in fact related to bottom line profitability.Sampling theory offers information and equations on how to take a representative sample from a process, however, the actual practice of extracting a sample from the process can be more of an art.Key sampling variables include product specifications such as bulk density, particle distribution, material conveyance, practical sampler location, sampler type and sample frequency and volume/mass.This paper discusses basic sampling theory, sampling methods, types of samplers, how to develop a ‘real world’ sampling system and current manual and automated samplers.Key points include ‘practical precision and variance’, material properties, flow properties, direct sampling versus indirect sampling, sampler types and applications.CITATION:Powalisz, J E, 2014. Bulk solids sampling, in Proceedings Sampling 2014 , pp 179–188 (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