Predicting Lignin Removal from Bark-inclusive Mixed-species Wood Chips via Ensemble Machine Learning

Abstract

This study investigated the prediction of lignin removal from mixed-species wood chips (oak and pine) containing bark. A two-stage pretreatment consisting of steam explosion (SE) and alkaline (NaOH) treatment was employed, and a dataset was constructed by systematically varying severity factors, bark inclusion, chemical concentration, and pretreatment time. Four predictive models—Random Forest (RF), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), and polynomial regression (PR)—were developed and evaluated using five repeated random 80:20 sample-level splits. XGBoost showed the highest mean test R² (0.9848), the lowest mean test RMSE (0.2984), and the highest mean cross-validation R² (0.9361), significantly outperforming conventional polynomial regression (R² = 0.5145). SHAP analysis identified NaOH concentration as the most influential predictor, followed by severity factor and bark inclusion. Finally, a web-based graphical user interface (GUI) was developed using Streamlit to provide real-time lignin removal predictions. These results showed that ensemble machine learning, particularly XGBoost, can effectively optimize pretreatment processes for heterogeneous biomass feedstocks in industrial biorefinery applications. These results support the use of ensemble machine learning for this heterogeneous feedstock, while indicating sensitivity to the sample-level partition and the need for future condition-level validation.