Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning

Accurate prediction of methane production in anaerobic digestion with various pretreatment strategies is of the utmost importance for efficient sludge treatment and resource recovery. Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. This work proposed a novel integrated system that employed an ensemble semisupervised learning (SSL)-automated ML (AutoML) model with limited variable inputs to reveal the effects of different pretreatments on methane production during sludge digestion with explainable analysis. Considering the direct correlations of the pretreatment type and digestion substrates, the pretreatment type is considered as a hidden variable. Results demonstrated that the AutoML model outperformed the conventional ML models (i.e., support vector regression (SVR), extreme gradient boosting (XGB), etc.), as evidenced by its higher R2 value. Moreover, the integration of SSL further enhanced the prediction accuracy by effectively leveraging unlabeled data, leading to a reduction in the mean squared error from 11.3 to 9.7. Explainable analysis results revealed the significance of different variables and the utmost importance of operating time, followed by proteins, carbohydrates, chemical oxygen demand, and volatile fatty acids. Furthermore, principal component and correlation analysis unveiled the interconnected relationships among substrate concentration, microbial communities, and metabolic functions for methane production and found that the increasing substrate concentration promoted the enrichment of functional microbial and metabolic functions. These insights shed light on the advantages of SSL-AutoML in predicting methane production in anaerobic digestion systems and elucidate the dependence relationships with key variables, offering valuable guidance for effective sludge pretreatment with enhanced resource recovery capabilities.