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Variability of the rock properties and the uncertainty associated with their estimation in the blocks mined and processed becloud the decision making in the mining value chain. In addition to grades of elements and minerals of interest, the presence of deleterious materials, as well as varying geometallurgical properties of rock materials have significant impacts on the economic performance of mining projects. Researchers are faced with the challenge that there are limited opportunities for benchmarking mining methods and technologies as this type of information is proprietary from mining companies and seldom shared for research purposes.

In this presentation, we discuss a methodology for building a high-resolution realistic synthetic block model containing grades as well as mineralogical and geometallurgical properties for benchmarking purposes. A high-resolution block model, featuring 128 million nodes, was developed for a porphyry copper deposit and used to simulate mining decisions through the mine value chain. To achieve this, samples were extracted from the model to allow for comparison of estimation and planning decisions with the actual model data, which provides access to the ground truth. Blocks of the high-resolution model were then averaged to different supports to learn about the impact of selectivity and to assess dilution, grade control, mine planning, and processing strategies. Applications of the developed approach to benchmark advanced mine planning techniques including machine-learning tools in prediction, forecasting, and decision making are also discussed.
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