ArticleRSC advances2026
Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions.
Article in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
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Abstract
Efficient recovery of copper from metallurgical waste is essential for sustainable resource utilization. This study develops an interpretable machine learning framework to predict copper leaching efficiency from copper slag under oxidative sulfuric acid conditions. A comprehensive multi-source dataset comprising 465 experimentally reported data points collected from multiple peer-reviewed studies was compiled from peer-reviewed literature. Four algorithms, Random Forest, Support Vector Regression, XGBoost, and LightGBM, were systematically optimized using 10-fold cross-validation. XGBoost demonstrated superior predictive performance with
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Registered trials
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