Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions
Abstract
= 0.9794, RMSE = 3.4757, and MAE = 2.3442 on the test set. SHAP-based interpretability analysis revealed that operational parameters, particularly leaching time, acid concentration, and temperature, exert dominant influence over copper extraction, while compositional variables such as Si, S, and Al show limited direct contribution within the investigated dataset range. The nonlinear trends identified are consistent with shrinking-core kinetics and diffusion-controlled mechanisms. External validation using independent literature datasets confirmed robust generalization capability. The proposed framework provides quantitative guidance for process optimization and offers a practical tool for enhancing sustainable metal recovery from metallurgical waste.
Fields of Study & Subjects
Metal Extraction and Bioleaching
Metallurgical Processes and Thermodynamics
Extraction and Separation Processes
Publication details
- Journal / source
- RSC Advances
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 16
- Issue
- 21
- Pages
- 19320–19333
- Publication date
- 1 January 2026
- Language
- EN