Prediction and optimization design of porous structure properties of biomass-derived biochar using machine learning methods

Biochar produced from biomass by pyrolysis and activation is a platform porous carbon material that has been widely used in many areas. The porosity properties of biochar such as specific surface area (SSA), total pore volume (Total_PV), micropore volume (Micro_PV), mesopore volume (Meso_PV), and average pore size (Average_PS) are essential to biochar applications. Although previous machine learning (ML) models can precisely predict SSA and Total_PV, these models are unable to comprehensively predict the other porosity characteristics. More importantly, activation, which is a critical process for preparing high-porosity biochar, was generally not considered in previous studies. Here, six single-target models were established first based on pyrolysis & activation conditions for the prediction of SSA, Total_PV, Micro_PV, Meso_PV, Average_PS, and yield, obtaining test R2 of 0.89, 0.86, 0.88, 0.89, 0.76 and 0.91, respectively. Then, a multi-target model was established for simultaneous prediction with an average test R2 of 0.87. ML model interpretation indicated agent type and ratio were crucial to porosity properties. Finally, activation and direct pyrolysis biochar production optimum schemes were derived from ML model for a high porosity. Favorable experimental verification results were obtained with validation R2 of 0.98, indicating the great potential of using ML for biochar engineering.