Predicting lignin removal efficiency in deep eutectic solvent-based biomass fractionation: an explainable machine learning approach

Abstract

Efficient and sustainable lignin extraction is essential for advancing green biorefinery processes. Deep eutectic solvents (DESs), as environmentally benign and tunable media, offer promising alternatives to conventional solvents, yet their rational design and optimization remain highly resource-intensive. In this study, we developed a novel machine learning-guided framework that integrates computational chemistry and data-driven modeling to accelerate the design of DES systems for lignocellulosic biomass pretreatment. A comprehensive dataset of 467 pretreatment conditions was constructed, featuring molecular descriptors and fingerprints to characterize DES chemical structures and key operational parameters. Among several models evaluated, the XGBoost model achieved the best predictive performance (R² = 0.8259, RMSE = 0.0928, MAE = 0.0672). SHAP analysis provided mechanistic insights, revealing the dominant influence of hydrogen bond donor–acceptor structures and solvent-to-solid ratio on lignin solubilization. The model demonstrated robustness when validated against an independent dataset (R² = 0.8034, MAE = 0.0661). This work offers a sustainable and mechanistically interpretable strategy for green solvent development, significantly reducing experimental workload and chemical waste, and contributing to advancing biomass valorization technologies.