Research Outputs (Mining Engineering)
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Item Sampling in the South African minerals industry.(The Southern African Institute of Mining and Metallurgy., 2014) Minnitt, R.C.A.Although not fully accepted in South Africa, the Theory of Sampling originally proposed by Pierre Gy is fast becoming the cornerstone of sampling practice throughout the world. The growing acceptance of Gy's Theory of Sampling in South Africa can be attributed to a number of factors, chief amongst them being the development of a tradable mineral asset market, the promulgation of the Mineral and Petroleum Resources Development Act (MPRDA), the growing number of commercial and academic courses that are offered on sampling, and the regulation of the industry through internationally acceptable guidelines and rules for reporting and trading in mineral assets. The size of the South African minerals industry and the dependence of our economy on mineral production have also meant that correct sampling is of key importance to mineral trade. ISO standards have been the principal guides for producers of mineral bulk commodities who produce to customers' specifications, whereas Gy's insights have been most readily accepted by precious and base metals producers whose product is sold into metal markets. Understanding of small-scale variability is essential in the precious and base-metal industries, but detailed studies of the effects of heterogeneity have not been as productive in the bulk commodities. Sampling practices at different stages of mineral development from exploration, face sampling and grade control, ore processing and handling, metallurgical sub-sampling, point of sale sampling, and sampling in the laboratory are considered in the gold, platinum, ferrous metal, and coal industries. A summary of the impact of poor sampling in these industries is presented. Generally it appears that poor sampling practice is most likely to erode mineral asset value at the early stages of mineral development. The benefits of good sampling are considered, especially with regard to the financial implications of bias and error on large and consistent consignments of bulk commodities.Item Cokriging for optimal mineral resource estimates in mining operations.(The Southern African Institute of Mining and Metallurgy., 2014) Minnitt, R.C.A.; Deutsch, C.V.Cokriging uses a sparsely sampled, but accurate and precise primary dataset, together with a more abundant secondary data-set, for example grades in a polymetallic orebody, containing both error and bias, to provide improved results compared to estimation with the primary data alone, as well as filtering the error and mitigating the effects of conditional bias. The method described here may also be applied in polymetallic orebodies and in other cases where the primary and secondary data could be collocated, and one of the data-sets need not be biased, unreliable, etc. An artificially created reference data-set of 512 lognormally distributed precious metal grades sampled at 25×25 m intervals constitutes the primary data-set. A secondary data-set on a 10×10 m grid comprising 3200 samples drawn from the reference data-set includes 30 per cent error and 1.5 multiplicative bias on each measurement. The primary and secondary non-collocated data-sets are statistically described and compared to the reference data-set. Variograms based on the primary data-set are modelled and used in the kriging of 10×10 m blocks using the 25×25 m and 50×50 m data grids for comparison against the results of the cokriged estimation. A linear model of coregionalization (LMC) is established using the primary and secondary data-sets and cokriging using both data-sets is shown to be a significant improvement over kriging with the primary data-set alone. The effects of the error and bias are filtered and removed during the cokriging estimation procedure. Thus cokriging using the more abundant secondary data, even though it contains error and bias, significantly improves the estimation of recoverable reserves.