Improving processing by adaption to conditional geostatistical simulation of block compositions
R Tolosana-Delgado, U Mueller, KG van den Boogaart, C Ward, J Gutzmer
Exploitation of an ore deposit can be optimized by adapting the beneficiation
processes to the properties of individual ore blocks. This can
involve switching in and out certain treatment steps, or setting their
controlling parameters. Optimizing this set of decisions requires the full
conditional distribution of all relevant physical parameters and chemical
attributes of the feed, including concentration of value elements and
abundance of penalty elements. As a first step towards adaptive
processing, the mapping of adaptive decisions is explored based on the
composition, in value and penalty elements, of the selective mining
units.
Conditional distributions at block support are derived from cokriging
and geostatistical simulation of log-ratios. A one-to-one log-ratio
transformation is applied to the data, followed by modelling via classical
multivariate geostatistical tools, and subsequent back-transforming of
predictions and simulations. Back-transformed point-support
simulations can then be averaged to obtain block averages that are fed
into the process chain model.
The approach is illustrated with a ‘toy’ example where a fourcomponent
system (a value element, two penalty elements, and some
liberable material) is beneficiated through a chain of technical
processes. The results show that a gain function based on full distributions
outperforms the more traditional approach of using unbiased
estimates.
Keywords: adaptive processing, change of suppport, compositions, geometallurgy,
stochastic optimization.