Abstract
Continued profitability in mineral resource extraction is challenged by depressed prices and decreasing grades, combined with increased extraction and processing costs, as well as the increasing depth and complexity of available deposits. A standard industry response to these challenges has been to adopt economies of scale; however, this approach is proven to have limitations in the current cost/price environment. Improved precision and accuracy in ore routing can overcome these challenges to a larger extent, but in order to achieve this, a new tool set consisting of high resolution data capture and modelling, coupled with flexible, real-time, online in-pit mineral classification appears to be required. This paper examines preliminary developments in measuring and modelling deposit heterogeneity at two copper mines in Chile. This examination is then followed by the development and use of a data model to evaluate the opportunity to introduce selective partitioning of ores through in-pit sensing and decision support tools prior to conventional processing in leach or flotation circuits. The results of this study suggest that the value proposition for the use of in-mine sensors for classifying and segregating valuable ore from waste, and to improve the accuracy of dispatch to the leaching, milling, or waste disposal stages, is significant.
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