Enhancing co-pyrolysis process of biomass and coal using machine learning insights and Shapley additive explanations based on cooperative game theory

Abstract

The co-pyrolysis process is an essential method for energy extraction from waste biomass and coal although the co-pyrolysis technology of biomass and coal presents a complex engineering challenge. To address these challenges, modern data-driven ensemble and tree-based machine learning approaches offer a promising solution. This study provides a comprehensive analysis of various machine learning techniques, including linear regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost) to predict the outcome models of pyrolysis oil yield, syngas yield, char yield, and syngas lower heating value from co-pyrolysis of biomass and coal. The models are evaluated using different statistical metrics. The DT-based pyrolysis oil yield model outperformed the other four models (LR, RF, XGBoost, and AdaBoost) in predicting pyrolysis oil with robust accuracy, achieving an R² of 0.999 and a mean squared error (MSE) close to zero during the model training phase. Similarly, the DT-based syngas yield model showed a high R² of 0.999 and near-zero MSE while the based char yield model excelled the others with a high R² of 0.999 and negligible MSE during the model training phase. In the subsequent phase, explainable artificial intelligence-based Shapley additive explanation (SHAP) values were estimated for feature importance analysis. The SHAP analysis identified key features for pyrolysis oil and syngas yield, with biomass blending ratio and reaction time being the most crucial, while reaction time and temperature were the most important for the syngas LHV model.