THE CHALLENGES OF ADAPTIVE PROCESSING TO GEOSTATISTICAL PREDICTIONS

World Mining Congress
Adaptive Processing as proposed in geometallurgy has to rely on spatially interpolated information on geometallurgical parameters like phase composition, size distributions of particles of different phases, grain shape parameters, and portions of value elements in different grains. Using the geostatistically predicted values for adaptive processing, e.g. for the selection of milling diameters, thresholds in physical separation, or choices on using an extra pre-separation step, is typically not optimal. Mathematically this effect is introduced by two forms of nonlinearities: 1) The nonlinear scales of compositions, distributions, and shapes have special properties with respect to geostatistics. Classical geostatistics creates some artefacts for these nonlinear scales. On the other hand, modern geostatistical procedures adapted to these scales do not provide unbiased results with respect to linear transformations of the data (e.g. biased block estimates). 2) Neither economic nor ecological effects (e.g. monetary gain) of processing decisions are linear in the interpolated geometallurgical parameters.
Keywords: Processing; Data; Distribution; Kriging; geostatistics; Model; Models; Prediction; Phase; Materials;
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