Machine learning-aided hydrothermal carbonization of biomass for coal-like hydrochar production: Parameters optimization and experimental verification
Quan Liu, Guanyu Zhang, Jiajia Yu, Ge Kong, Tianqi Cao, Guanya Ji, Xuesong Zhang, Lujia Han
Abstract
Biomass to coal-like hydrochar via hydrothermal carbonization (HTC) is a promising route for sustainability development. Yet conventional experimental method is time-consuming and costly to optimize HTC conditions and characterize hydrochar. Herein, machine learning was employed to predict the fuel properties of hydrochar. Random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) models were developed, presenting acceptable prediction performance with R 2 at 0.825–––0.985 and root mean square error (RMSE) at 1.119–––5.426, and XGB outperformed RF and SVM. The model interpretation indicated feedstock ash content, reaction temperature, and solid to liquid ratio were the three decisive factors. The optimized XGB multi-task model via feature re-examination illustrated improved generalization ability with R 2 at 0.927 and RMSE at 3.279. Besides, the parameters optimization and experimental verification with wheat straw as feedstock further demonstrated the huge application potential of machine learning in hydrochar engineering.