Geometallurgy

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,:

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.

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. 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.

The following tests are commonly used for geometallurgical modeling:

Block kriging is the most common geostatistical method used for interpolating metallurgical index parameters and it is often applied on a domain basis. Classical geostatistics require that the estimation variable be additive, and there is currently some debate on the additive nature of the metallurgical index parameters measured by the above tests. The SPI(r) value is known not to be an additive parameter, however errors introduced by block kriging are not thought to be significant . These issues, among others, are being investigated as part of the Amira P843 research program on Geometallurgical mapping and mine modelling.

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