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.

Deep learning-assisted automated sewage pipe defect detection for urban water environment management.

A healthy sewage pipe system plays a significant role in urban water management by collecting and transporting wastewater and stormwater, which can be assessed by hydraulic model. However, sewage pipe defects have been observed frequently in recent years during regular pipe maintenance according to the captured interior videos of underground pipes by closed-circuit television (CCTV) robots. In this case, hydraulic model constructed based on a healthy pipe would produce large deviations with that in real hydraulic performance and even be out of work, which can result in unanticipated damages such as blockage collapse or stormwater overflows. Quick defect evaluation and defect quantification are the precondition to achieve risk assessment and model calibration of urban water management, but currently pipe defects assessment still largely relies on technicians to check the CCTV videos/images. An automated sewage pipe defect detection system is necessary to timely determine pipe issues and then rehabilitate or renew sewage pipes, while the rapid development of deep learning especially in recent five years provides a fantastic opportunity to construct automated pipe defect detection system by image recognition. Given the initial success of deep learning application in CCTV interpretation, the review (i) integrated the methodological framework of automated sewage pipe defect detection, including data acquisition, image pre-processing, feature extraction, model construction and evaluation metrics, (ii) discussed the state-of-the-art performance of deep learning in pipe defects classification, location, and severity rating evaluation (e.g., up to ~96 % of accuracy and 140 FPS of processing speed), and (iii) proposed risk assessment and model calibration in urban water management by considering pipe defects. This review introduces a novel practical application-oriented methodology including defect data acquisition by CCTV, model construction by deep learning, and model application, provides references for further improving accuracy and generalization ability of urban water management models in practical application.

Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization.

In the context of advocating carbon neutrality, there are new requirements for sustainable management of municipal sludge (MS). Hydrothermal carbonization (HTC) is a promising technology to deal with high-moisture MS considering its low energy consumption (without drying pretreatment) and value-added products (i.e., hydrochar). This study applied machine learning (ML) methods to conduct a holistic assessment with higher heating value (HHV) of hydrochar, carbon recovery (CR), and energy recovery (ER) as model targets, yielding accurate prediction models with R2 of 0.983, 0.844 and 0.858, respectively. Furthermore, MS properties showed positive (e.g., carbon content, HHV) and negative (e.g., ash content, O/C, and N/C) influences on the hydrochar HHV. By comparison, HTC parameters play a critical role for CR (51.7%) and ER (52.5%) prediction. The primary sludge was an optimal HTC feedstock while anaerobic digestion sludge had the lowest potential. This study provided a comprehensive reference for sustainable MS treatment and industrial application.