Machine Learning Framework for Carbon Purification from Hazardous Spent Cathode Carbon via LightGBM Hyperparameter Optimization

Spent cathode carbon (SCC), a hazardous waste generated during primary aluminium production, contains valuable graphitic carbon resources but remains difficult to recycle because carbon purification is governed by complex interactions among multiple leaching parameters. Conventional process optimization relies on extensive laboratory experimentation, resulting in high chemical consumption, energy use, and development costs. This study presents an explainable machine learning framework for cleaner and more resource-efficient carbon purification from SCC under limited-data conditions. Six machine learning algorithms (GBDT, CatBoost, XGBoost, LightGBM, Random Forest, and Decision Tree) were evaluated using experimental data from alkaline leaching. The LightGBM model was systematically optimized by combining Response Surface Method (RSM), Orthogonal Experimental Design (OED), and local parameter optimization methods. The optimized model (min_child_samples = 2, num_leaves = 32, n_estimators = 500, and learning_rate = 0.5) achieved an R2 of 0.8015, RMSE of 0.9163, and MAE of 0.6599. SHAP analysis identified initial alkali concentration, liquid–solid ratio, stirring rate, and leaching time as the dominant factors controlling carbon purification, whereas temperature had a comparatively smaller influence within the investigated operating range. The results indicate that improving reagent utilization and hydrodynamic conditions offers greater potential for enhancing carbon purification than increasing thermal input alone. By integrating statistical experimental design with explainable machine learning, this study establishes an efficient, interpretable, and transferable decision-support framework for optimizing hazardous waste recycling and other resource recovery processes under limited-data conditions, thereby supporting cleaner production and circular economy practices.