Machine learning assisted modelling of anaerobic digestion of waste activated sludge coupled with hydrothermal pre-treatment

Abstract

This study utilizes decision-tree-based models, including Random Forest, XGBoost, artificial neural networks (ANNs), support vector machine regressors, and K nearest neighbors algorithms, to predict sludge solubilization and methane yield in hydrothermal pretreatment (HTP) coupled with anaerobic digestion (AD) processes. Analyzing two decades of published research, we find that ANN models exhibit superior fitting accuracy for solubilization prediction, while decision-tree models excel in methane yield prediction. Pretreatment temperature is identified as pivotal among various variables, and heating time surprisingly emerges as equally significant as holding time for solubilization and surpasses it for methane yield. Contrary to prior expectations, the HTP method's impact on sludge solubilization and AD performance is minimal. This study underscores data-driven models' potential as resource-efficient tools for optimizing advanced AD processes with HTP. Notably, our research spans nearly two decades of lab, pilot, and full-scale studies, offering novel insights not previously explored.