Improving processing by adaption to conditional geostatistical simulation of block compositions

Abstract

One of the key usages of geostatistics has long been the prediction of the spatial structure of orebodies. This is used for the evaluation of resources and/or reserves and for further planning of the mining and beneficiation process schedule. For most applications, and until quite recently, metal grade has been regarded as the central property of study and the main objective has been to distinguish between ore and waste. However, recently other properties have come into focus through better analytical methods, such as automated mineralogy (see e.g. Fandrich et al. 2007), and new geostatistical methods considering more complex information, such as kriging of compositions (Pawlowsky-Glahn and Olea, 2004). Two ores with the same chemical composition can have totally different mineralogies and microfabrics, which will result in different recoveries, energy requirements, or reagent consumptions, thus yielding very different mass streams. An ore is thus no longer understood as represented by a single value element, but through a complex microfabric (Hagni, 2008). This perspective allows quantitative insight into relevant properties of different ore and gangue minerals (Sutherland and Gottlieb, 1991), potentially containing poison elements or phases (e.g. Houot, 1983) that modify the efficiency of downstream processing steps or require additional treatment. Accordingly, processing choices have become more complex. Grade-based studies allow a mere ‘beneficiate-or-dump’ decision. With the advent of these analytical and methodological advances, it is possible to better adapt processing to the ore mined due to a more profound understanding of the processing required. For instance, good knowledge of microfabric properties can reduce energy consumption, if overgrinding is avoided, which also results in improved recovery by avoiding losses due to poor liberation (Wills and Napier-Munn, 2006). Similarly, accurate information on mineral composition may permit the specification of a cut-off in any separation technique (magnetic, electrostatic, or density-based), optimally weighting recovery and further processing costs. Or depending on the proportion of fines generated during milling, desliming might or might not be necessary. Finally, different concentrations of chemicals might be needed for an optimal flotation process as a function of the composition of the concentrate. These Improving processing by adaption to conditional geostatistical simulation of block compositions