Gene expression programming for modeling and predicting of leaching of chalcopyrite concentrates using 1-hexyl-3-methyl-imidazolium hydrogen sulfate ionic liquid aqueous solution

Abstract

This study aims to develop a precise predictive model for leaching chalcopyrite concentrates. It employs a leaching system comprising 1-hexyl-3-methylimidazolium hydrogen sulfate ([Hmim][HSO₄]) and hydrogen peroxide (H₂O₂), offering a more efficient alternative to conventional hydrometallurgical approaches. Gene expression programming (GEP) was used to develop this model. To construct these GEP models, 120 experimental data points were collected initially. Input variables included time, acid concentration, temperature, particle size, oxidant concentration, stirring speed, and solid/liquid ratio, while output variables included copper extraction percentage. For modeling purposes, the experimental dataset was randomly partitioned into a training set (84 data points) and a testing set (36 data points). A correlation analysis (BCA) revealed weak linear correlations between input variables, justifying the use of advanced methods such as GEP. Using criteria such as coefficient of determination (R²), mean absolute error (MAE), and root relative square error (RRSE), we proposed the optimal model (GEP-3). As a new model with simplified mathematical expressions for accurate prediction of copper extraction from chalcopyrite concentrate, this model achieves R² = 0.976, MAE = 2.80, and RRSE = 0.152 in the training set. Sensitivity analysis revealed that temperature, oxidant concentration, and particle size were the most influential parameters on the copper extraction percentage. By taking into account practical or economic constraints, the proposed model enables the optimization of the leaching process to maximize copper extraction and minimize material consumption.