Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability

The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling.