Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions

Sung-Jin Kim; Song-Sae Kang; Kyong-Nam Pae; Song-Il Pak; Hyon-Il Jo; Ryong-Jin Kim

doi:10.1039/d6ra01571a

January 1, 2026RSC AdvancesOpen Access

Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions

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Key Points

The aim is to model and predict the efficiency of copper leaching from slag using machine learning.
Utilized an interpretable XGBoost model for prediction.
Conducted SHAP analysis to identify influential factors.
Analyzed the effect of time, acid concentration, and temperature.
Achieved a predictive model with R² = 0.9794.
Identified time, acid, and temperature as dominant factors affecting leaching efficiency.
Enabled quantitative optimization for sustainable copper recovery.

Implication

Machine learning predicts copper leaching efficiency in slag, indicating potential for process optimization.

Abstract

An interpretable XGBoost model predicts copper leaching efficiency from slag with R 2 = 0.9794. SHAP analysis shows time, acid, and temperature dominate, enabling quantitative process optimization for sustainable recovery.

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Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69df2c1de4eeef8a2a6b10bc https://doi.org/10.1039/d6ra01571a

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Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions

Key Points

Implication

Abstract

Discussion

Cite This Study

Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions

Key Points

Implication

Abstract

Discussion

Cite This Study

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