Machine learning prediction of higher heating value of biochar based on biomass characteristics and pyrolysis conditions

Abstract

The higher heating value of biochar is an important parameter for the utilization of biomass energy. In this work, extreme gradient boosting regression and artificial neural network were used to predict it based on the characteristics of biomass and pyrolysis conditions. Besides, empirical correlations were developed for comparison. Results showed that the extreme gradient boosting regression models showed better performance (R2 = 0.83–0.94). The shapley additive explanations and partial dependence plot indicated that lignin content and higher heating value of raw material were highly positively correlated with higher heating value of biochar, and found the better conditions such as pyrolysis temperature (>550 °C), lignin content (>40 wt%) for high-higher heating value biochar preparation. What’s more, a program that predicted higher heating value of biochar was developed through PySimpleGUI library. It offered a new optimization idea for the directional preparation process of biochar.