Machine learning prediction of specific capacitance in biomass derived carbon materials: Effects of activation and biochar characteristics

Abstract

The preparation process of biomass-based biochar materials is usually screened using traditional trial-and-error experiments. In this approach, the electrochemical properties of biochar are correlated with properties called descriptors. In this work, several simple and efficient machine learning (ML) models were used to predict the electrical capacity of biochar through activation conditions, biochar properties, and testing conditions. The established ML model predicted the capacitance of biochar with 9 descriptors that are readily available values during the preparation of biochar. The prediction performance of four regression methods (Decision Tree (DT), Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost) and Random Forest (RF)) were evaluated with a test set/training set ratio of 8 to 2. Among the four regression methods, XGBoost had the best prediction effect on the electrochemical performance of biochar with a low mean root mean square error (RMSE) and coefficient of determination (R2) close to 1. In addition, the analysis of the importance of the features under each model combined with the existing research verifies the rationality of the model. The accuracy and simplicity of this system demonstrate that the electrochemical performance of biochar can be easily predicted without time-consuming traditional experimental procedures and can be a method to guide the direction of experiments.