Prediction of Biochar Yield and Specific Surface Area Based on Integrated Learning Algorithm

Abstract

Biochar is a biomaterial obtained by pyrolysis with high porosity and high specific surface area (SSA), which is widely used in several fields. The yield of biochar has an important effect on production cost and utilization efficiency, while SSA plays a key role in adsorption, catalysis, and pollutant removal. The preparation of biochar materials with better SSA is currently one of the frontiers in this research field. However, traditional methods are time consuming and laborious, so this paper developed a machine learning model to predict and study the properties of biochar efficiently for engineering through cross-validation and hyper parameter tuning. This paper used 622 data samples to predict the yield and SSA of biochar and selected eXtreme Gradient Boosting (XGBoost) as the model due to its excellent performance in terms of performance (yield correlation coefficient R2 = 0.79 and SSA correlation coefficient R2 = 0.92) and analyzed it using Shapley Additive Explanation. Using the Pearson correlation coefficient matrix revealed the correlations between the input parameters and the biochar yield and SSA. Results showed the important features affecting biochar yield were temperature and biomass feedstock, while the important features affecting SSA were ash and retention time. The XGBoost model developed provides new application scenarios and ideas for predicting biochar yield and SSA in response to the characteristic input parameters of biochar.

Keywords

• biomass pyrolysis
• integrated learning
• model prediction
• pyrolysis product