Abstract

Currently, chloride leaching is the most efficient technique available for copper recovery in the low-grade mining segment (below 0.4 % CuT). No better biological or hybrid alternative processes have been found to date. However, there is still a lack of knowledge regarding the optimal operational and design parameters for leaching heaps to ensure sustainability. Identifying optimal operational values involves determining the optimal dosages of water, sodium chloride, and/or calcium chloride, as well as the optimal temperatures for the process at various stages, aeration requirements for the heaps, input and output humidity, among other factors. This work proposes a machine learning approach to diagnose the health status of a chloride leaching heap and to forecast copper recovery levels of copper chloride leaching process.