Sulphate-Chloride Leaching of Chalcopyrite: Process Optimization and Predictive Computational Modeling Using Gaussian Process Regression

Chalcopyrite leaching in sulfuric acid with chloride-based oxidizing agents (NaCl, NaClO, NaClO3) was investigated to optimize copper recovery. The influence of sulfuric acid concentration, chloride concentration, and temperature on copper dissolution was systematically evaluated through experimental tests. A Gaussian Process Regression (GPR) model was developed to predict copper recovery, integrating experimental data with Partial Rank Correlation Coefficient (PRCC) analysis to assess the impact of key variables. The results showed that NaClO and NaClO3 significantly improved copper recovery, with NaClO3 achieving nearly 100% copper recovery in under 30 min at higher temperatures. Maximum recovery of 45.5% was achieved with NaCl at 1 M concentration, 3 M H2SO4, and 80 °C. The GPR model demonstrated superior predictive accuracy, achieving RMSE = 4.0028 and R2 = 0.99, outperforming Support Vector Machine Regression (SVMR) and Ensemble Regression (ER) models. The GPR model accurately predicted recovery under conditions not tested experimentally, providing a robust tool for process optimization. The results confirm the effectiveness of chloride-based oxidizers in enhancing copper dissolution and demonstrate the practical application of GPR for optimizing leaching conditions, ensuring maximum copper recovery in hydrometallurgical processes.