Copper mining activity is going through big changes due to increasing technological development in the area and the influence of\nindustry 4.0. These changes, produced by technological context and more controls (e.g., environmental controls), are also becoming\nvisible in Chilean mining. New regulations from the Chilean government and changes in the copper mining industry (such as a\ntrend to underground mining) are fostering the search for better results in typical processes such as leaching. This paper\ndescribes an experience using artificial intelligence techniques, particularly random forest, to develop predictive models for\ncopper recovery by leaching, using data from an enterprise present in northern Chile for more than 20 years. Two models, one\nof them with actual operational data and another one with data generated in a controlled environment (piling) are presented.\nWell-classified values of 98.90% for operational data and 98.72% for pile/piling data were obtained. The methodology devised\nfor the study can be transferred to piling columns or piles with other characteristics, though the operation must focus on copper\nleaching. It can even be transferred to other leaching processes using another type of mineral, with proper adjustments.
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