Machine learning assisted prediction of specific surface area and nitrogen content of biochar based on biomass type and pyrolysis conditions

Predicting and optimizing the physicochemical properties of biochar is crucial for its applications. The characteristics of biomass and pyrolysis conditions are the main factors influencing these properties. However, the numerous components of biomass and the pyrolysis conditions contribute to the substantial challenge in predicting the physicochemical properties, particularly the specific surface area and the nitrogen content of biochar. In this work, machine learning methods including random forest (RF), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGB) (all with R² exceeding 0.97) were used to predict and analyze specific surface area of biochar (SSA), N content of biochar (N-char), and yield of biochar (Yield-char). Compositions of biomass and pyrolysis conditions were selected as input variables. The partial dependence plot analysis showed the impact way of each influential factor on the target variable and the interactions among these factors in the pyrolysis process. The feature importance of these models indicated that the influencing factors toward predicting three targets (sorted by importance) were specified as follows: pyrolysis temperature, nitrogen content, and fixed carbon for Yield-char; N and ash for N-char; ash and pyrolysis temperature for SSA. This work provided new insights for understanding pyrolysis process of biomass.