Abstract

The mining sector actively seeks to improve operational processes and manage residual materials, especially in areas used for heap leaching disposal. The flowability of residues following deposition can have an impact on storage capacity, productivity, and workers' safety. In this study, an artificial neural network (ANN) approach is applied to evaluate the accuracy of three models in predicting the flowability of spent heap leach when it is discharged into the dump, considering three or five segregation categories. The models with five categories exhibited the highest level of accuracy, with learning responses ranging from 72% to 78% and predictions from 88% to 96%. These indicate that ANN models have the potential to be a decision-making tool for the discharge strategy in the dump. Modules containing lithologies such as clays and phyllosilicates exhibited increased susceptibility to separation due to their water retention capacity, which negatively impacted their permeability and conductivity. The decomposition of iron oxide, along with clays and low hardness, led to the formation of fines, limited permeability, and inadequate solution drainage. Rock competence and low formation of fines provide good permeability, and better drainage conditions for the solution, and help maintain the stability of the spent heap leach in the dump.