한빛사논문
- 조회484
Jie Li a,1, Manu Suvarna a,b,1, Lanyu Li c, Lanjia Pan d, Javier Pérez-Ramírez b, Yong Sik Ok e, Xiaonan Wang f,c
aDepartment of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
bInstitute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland
cDepartment of Chemical Engineering, Tsinghua University, Beijing, 100084, China
dXiamen Municipal Environment Technology Co.,Ltd, Xiamen, 361021, China
eKorea Biochar Research Center, APRU Sustainable Waste Management Program and Division of Environmental Science and Ecological Engineering, Korea University, Seoul, South Korea
fKey Laboratory of Industrial Biocatalysis (Tsinghua University), the Ministry of Education, Beijing, 100084, China
1These authors contributed equally to this work.
Corresponding authors: Yong Sik Ok, Xiaonan Wang
Abstract
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.
논문정보
- Research article
- 2022년 09월 (BRIC 등록일 2023-07-26)
- 국내(교신)+국외 연구진
- 바이오·의료융합 > 바이오소재
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