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- W4313016825 abstract "This article presents a new geometallurgical framework that builds empirical prediction models of key performance indicators of the processing plant and integrates them into a simultaneous stochastic optimization model for mine production scheduling. The prediction models are created by tracking blended rock properties and matching them with observed responses from the operating processing plant. Several applied case studies in a gold mining complex are presented building prediction models of ball mill throughput, steel ball consumptions, and reagent consumptions from collected data in a gold leaching operation. Finally, the integration of these geometallurgical prediction models into stochastic mine production scheduling is demonstrated to monetize the gained insights. Compared with conventional planning, the resulting mine plans increase yearly profit and are more likely to align with production targets." @default.
- W4313016825 created "2023-01-05" @default.
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- W4313016825 date "2022-01-01" @default.
- W4313016825 modified "2023-09-30" @default.
- W4313016825 title "Geometallurgical prediction models of processing plant indicators for stochastic mine production scheduling" @default.
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- W4313016825 doi "https://doi.org/10.1016/j.ifacol.2022.09.261" @default.
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