The conversion of wet waste (e.g., food waste, sewage sludge, and animal manure) into bioenergy is a promising strategy for sustainable energy generation and waste management. Although experimental efforts have driven waste conversion technologies (WCTs) to various degrees of maturity, computational modeling has equally contributed to this endeavor. This review focuses on the application of modeling techniques, including computational fluid dynamics (CFD), process simulation (PS), and machine learning (ML) on WCTs including anaerobic digestion, hydrothermal carbonization, gasification, pyrolysis and incineration. It addresses in a concise manner on how CFD models aid in understanding of the complex process and their molecular kinetics; while PS and ML models help in understanding the reaction kinetics, variable-response relationship, techno-economic assessment and sensitivity analysis. Relevant modeling approaches with their pros and cons are summarized and case studies are presented for each WCT. Moreover, a comparative evaluation among the three modeling techniques, along with their recent and ongoing developments are highlighted. Hybrid frameworks derived by combining mechanistic and ML models are proposed, which are expected to advance future wet waste valorization strategies for sustainable clean energy production and waste management.
Bibliographical noteFunding Information:
This work was supported by the Key Laboratory of Industrial Biocatalysis (Tsinghua University) , the Ministry of Education , China. The authors acknowledge the National Research Foundation , the Prime Minister's Office (Singapore) under its Campus for Research Excellence and Technological Enterprise (CREATE) program (Grant No. R-706-000-103-281 and R-706-001-102-281 ), the IAF-PP grant titled “Cyber-physical production system (CPPS) towards contextual and intelligent response” by the Agency for Science, Technology, and Research (Grant No. A19C1a0018 ), the National Research Foundation of Korea (NRF) grant funded by the Korea government ( MSIT ) (No. 2021R1A2C2011734 ), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF-2021R1A6A1A10045235 ).
© 2022 Elsevier Ltd
- Anaerobic fermentation
- Computational fluid dynamics
- Machine learning
- Process modeling
- Sustainable energy
- Thermal conversion
ASJC Scopus subject areas
- Environmental Science(all)
- Industrial and Manufacturing Engineering
- Renewable Energy, Sustainability and the Environment
- Strategy and Management