A catalyst-aware explainable machine learning framework for biodiesel yield prediction over metal-doped biochar and activated carbon catalysts

Abstract

Biodiesel production over metal-doped biochar and activated carbon (AC) catalysts involves complex nonlinear interactions among feedstock characteristics, catalyst descriptors, and operating conditions, making accurate yield prediction a challenging task. While machine learning (ML) has shown potential in process modeling, existing studies lack catalyst-aware frameworks that integrate material and process descriptors within a unified representation for biodiesel yield prediction. Furthermore, current approaches are often limited by small datasets and insufficient model interpretability, restricting their ability to support reliable catalyst screening and process optimization. To address these challenges, this study develops an explainable ML framework for biodiesel yield prediction using a literature-derived dataset of metal-doped biochar and AC catalyst systems. The framework integrates catalyst, feedstock, and operating-condition descriptors, augments sparse experimental data through curve digitization, evaluates six ML models, and applies SHAP and CatBoost-based explainability analysis. The neural network model achieved the highest predictive accuracy on unseen data, with RMSE of 3.27%, MAE of 1.64%, and R2 of 0.95, whereas linear regression showed the weakest performance, highlighting the nonlinear behavior of the catalytic system. Validation using an independent experimental dataset further confirmed model generalization. Explainability analysis identified alcohol-to-oil ratio, reaction time, catalyst amount, reaction temperature, and feedstock acid value as the key factors governing biodiesel yield. The proposed ML framework provides an accurate and interpretable approach for catalyst screening and data-driven optimization of sustainable biodiesel production processes.