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Geometallurgy relates to the practice of combining geology or geostatistics with metallurgy, or, more specifically, extractive metallurgy, to create a spatially or geologically based predictive model for mineral processing plants. It is used in the hard rock mining industry for risk management and mitigation during mineral processing plant design. It is also used, to a lesser extent, for production planning in more variable ore deposits. There are four important components or steps to developing a geometallurgical program,:〔Bulled, D., and McInnes, C: Flotation plant design and production planning through geometallurgical modeling. Centenary of Flotation Symposium, Brisbane, QLD, 6-9. June 2005.〕 *the geologically informed selection of a number of ore samples *laboratory-scale test work to determine the ore's response to mineral processing unit operations *the distribution of these parameters throughout the orebody using an accepted geostatistical technique *the application of a mining sequence plan and mineral processing models to generate a prediction of the process plant behavior ==Sample selection== The sample mass and size distribution requirements are dictated by the kind of mathematical model that will be used to simulate the process plant, and the test work required to provide the appropriate model parameters. Flotation testing usually requires several kg of sample and grinding/hardness testing can required between 2 and 300 kg.〔McKen, A., and Williams, S.: An overview of the small-scale tests required to characterize ore grindability. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006〕 The sample selection procedure is performed to optimize granularity, sample support, and cost. Samples are usually core samples composited over the height of the mining bench.〔Amelunxen, P. et al: Use of geostatistics to generate an orebody hardness dataset and to quantify the relationship between sample spacing and the precision of the throughput estimate. Autogenous and Semi-Autogenous Grinding Technology 2001, Vancouver, Canada, 2006〕 For hardness parameters, the variogram often increases rapidly near the origin and can reach the sill at distances significantly smaller than the typical drill hole collar spacing. For this reason the incremental model precision due to additional test work is often simply a consequence of the central limit theorem, and secondary correlations are sought to increase the precision without incurring additional sampling and testing costs. These secondary correlations can involve multi-variable regression analysis with other, non-metallurgical, ore parameters and/or domaining by rock type, lithology, alteration, mineralogy, or structural domains.〔Amelunxen, P.: The application of the SAG Power Index to ore body hardness characterization for the design and optimization of comminution circuits, M. Eng. Thesis, Department of Mining, Metals and Materials Engineering, McGill University, Montreal, Canada, Oct. 2003. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006〕〔Preece, Richard. Use of point samples to estimate the spatial distribution of hardness in the Escondida porphyry copper deposit, Chile. International Autogenous and Semi-Autogenous Grinding Technology 2006, Vancouver, Canada, 2006〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Geometallurgy」の詳細全文を読む スポンサード リンク
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