Machine learning estimation of CO₂ sequestration for mineralized artificial aggregates derived from industrial solid wastes

CO₂ mineralization of artificial aggregates derived from industrial solid wastes serves as one of the most promising strategies for simultaneous solid waste valorization and carbon sequestration. However, accurate prediction of CO₂ sequestration is challenging due to the complex nonlinear interactions between raw material properties and process parameters. In this study, a data-driven machine learning (ML) framework based on experimental datasets from reviewed studies was developed. Furthermore, six ML models including Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) were evaluated with data augmentation and Bayesian hyperparameter optimization. The tree-based ensemble models achieved outstanding prediction performance, with test-set R² values of 0.92-0.93 and small deviations from the cross-validation results, demonstrating high accuracy and stable generalization ability. SHapley Additive exPlanations (SHAP) analysis revealed that calcium oxide content, silica content, particle size distribution, and CO₂ concentration were the dominant features affecting CO₂ sequestration. Silica had an inhibitory effect on CO₂ sequestration by forming calcium silicate hydrate (C-S-H) at a low Ca/Si ratio during hydration. Finally, a graphical user interface integrated with the optimal models was constructed to quantitatively evaluate CO₂ sequestration in engineering applications. This study provides a transferable and interpretable framework for the design of low carbon artificial aggregates, supporting the synergistic valorization of industrial solid wastes and CO₂ sequestration.