Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics

A general method for predicting gas yield is crucial in biomass and plastics co-pyrolysis. This study employed two machine learning methods to forecast gas yield in co-pyrolysis. Comparing the predictive performance of Support Vector Regression (SVR) with an R² of 0.72 and a root mean square error (RMSE) of 0.15, while eXtreme Gradient Boosting (XGBoost) demonstrated a superior performance with an R² of 0.90 and an RMSE of 0.08. Therefore, XGBoost was selected as the final prediction model. Results obtained from the machine learning interpretation tool, SHapley Additive exPlanations (SHAP), revealed that the two most influential factors affecting gas yield were the highest co-pyrolysis temperature (HTT) and the blending ratio (BR), contributing 33% and 28% to the model's predictions, respectively. Besides, the moisture content in biomass (MB) has been found to be one of the critical variables affecting the gaseous products yield. To determine the interaction between these factors and their contributions to gas yield, SHAP partial dependence analysis (SHAP PDA) was conducted. Therefore, this study offers novel insights into predicting gas yields in biomass and plastics co-pyrolysis, aiding in identifying optimal conditions for maximizing gas yield production.