Machine Learning-Driven Optimization for Predicting Biochar Adsorption Performance Toward Pb(II) and Cd(II)

With the increasing levels of toxic heavy metals such as Pb(II) and Cd(II), their discharge poses serious threats to environmental safety and human health, necessitating efficient remediation technologies. Biochar has emerged as a promising eco-friendly adsorbent; however, its adsorption performance is constrained by interactions among material properties, environmental conditions, and ion specificity. Conventional machine learning (ML) models are typically built on single-metal-ion datasets, limiting their ability to leverage shared information across related adsorption scenarios. To address this limitation, this study proposes a descriptor-based ML framework for Pb(II)–Cd(II) adsorption prediction, in which ion-related physicochemical descriptors, such as electronegativity and hydrated ionic radius, are incorporated in place of discrete ion labels to enable ion-specific modeling. An Optuna-optimized CatBoost model achieved high predictive accuracy (R2 = 0.952, RMSE = 9.80) and demonstrated improved performance on both Pb and Cd subsets compared with single-ion models. SHAP analysis reveals the model is consistent with known adsorption-related factors. Uncertainty quantification was incorporated to constrain predictions and enhance robustness. Ultimately, this study provides a robust data-driven baseline for heavy metal adsorption modeling, offering mechanistic insights into biochar–metal interactions and demonstrating a physicochemical descriptor approach that supports future extensions to broader multi-ion systems.