Document Type
Journal Article
Publisher
Southern African Institute of Mining and Metallurgy (SAIMM)
School
School of Engineering
RAS ID
19878
Abstract
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 four-component 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.
Additional Information
This work was funded through the base funding of the Helmholtz Institute Freiberg for Resource Technology by the German Federal Ministry for Research and Education and the Free State of Saxony.
Access Rights
free_to_read
Comments
This is an Author's Accepted Manuscript of: Tolosana-Delgado, R., Mueller, U., Van Boogaart, K.G.D., Ward, C., Gutzmer, J. (2015). Improving processing by adaption to conditional geostatistical simulation of block compositions in Journal of the Southern African Institute of Mining and Metallurgy, 115(1), 13-26. Available here.