Wet wastes to bioenergy and biochar: A critical review with future perspectives
- Authors
- Li, Jie; Li, Lanyu; Suvarna, Manu; Pan, Lanjia; Tabatabaei, Meisam; Ok, Yong Sik; Wang, Xiaonan
- Issue Date
- 15-4월-2022
- Publisher
- ELSEVIER
- Keywords
- Waste to energy and resource; Biological and thermal conversion; Charcoal; Carbon sequestration; Clean energy; Sustainable development goals
- Citation
- SCIENCE OF THE TOTAL ENVIRONMENT, v.817
- Indexed
- SCIE
SCOPUS
- Journal Title
- SCIENCE OF THE TOTAL ENVIRONMENT
- Volume
- 817
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/143097
- DOI
- 10.1016/j.scitotenv.2022.152921
- ISSN
- 0048-9697
- Abstract
- The ever-increasing rise in the global population coupled with rapid urbanization demands considerable consumption of fossil fuel, food, and water. This in turn leads to energy depletion, greenhouse gas emissions and wet wastes gener-ation (including food waste, animal manure, and sewage sludge). Conversion of the wet wastes to bioenergy and bio-char is a promising approach to mitigate wastes, emissions and energy depletion, and simultaneously promotes sustainability and circular economy. In this study, various conversion technologies for transformation of wet wastes to bioenergy and biochar, including anaerobic digestion, gasification, incineration, hydrothermal carbonization, hy-drothermal liquefaction, slow and fast pyrolysis, are comprehensively reviewed. The technological challenges imped-ing the widespread adoption of these wet waste conversion technologies are critically examined. Eventually, the study presents insightful recommendations for the technological advancements and wider acceptance of these processes by establishing a hierarchy of factors dictating their performance. These include: i) life-cycle assessment of these conver-sion technologies with the consideration of reactor design and catalyst utilization from lab to plant level; ii) process intensification by integrating one or more of the wet waste conversion technologies for improved performance and sus-tainability; and iii) emerging machine learning modeling is a promising strategy to aid the product characterization and optimization of system design for the specific to the bioenergy or biochar application.
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Collections - College of Life Sciences and Biotechnology > Division of Environmental Science and Ecological Engineering > 1. Journal Articles
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