Critical insights into ensemble learning with decision trees for the prediction of biochar yield and higher heating value from pyrolysis of biomass

Pyrolysis is an efficient thermochemical conversion process, but accurate prediction of yield and properties of biochar presents a significant challenge. Three prominent ensemble learning methods, viz. Random Forest (RF), eXtreme Gradient Boosting (XGB), and Adaptive Boosting (AdaBoost) were utilized to develop models to predict yield and higher heating value (HHV) of biochar. Dataset comprising 423 observations from 44 different biomasses was curated from peer-reviewed journals for predicting biochar yield. RF regressor achieved a test R2 of 0.86 for biochar yield, while XGB regressor achieved a test R2 of 0.87 for biochar HHV prediction. The SHapley Additive exPlanations (SHAP) analysis was conducted to assess influence of each feature on the model's output. Pyrolysis temperature and ash content of biomass were identified as the most influential features for the prediction of both yield and HHV of biochar. The partial dependence plots (PDPs) revealed nonlinear relationships, interpreting how the model formulates its predictions.