Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges
文献类型：期刊论文
| 作者 | Huang, Li-ting1,2; Hou, Jia-yi1; Liu, Hong-tao1 |
| 刊名 | WASTE MANAGEMENT
 |
| 出版日期 | 2024-04-15 |
| 卷号 | 178页码:155-167 |
| 关键词 | Machine -learning intervention Organic waste composting Composting quality Composting maturity Aerobic composting |
| ISSN号 | 0956-053X |
| DOI | 10.1016/j.wasman.2024.02.022 |
| 通讯作者 | Liu, Hong-tao(liuht@igsnrr.ac.cn) |
| 英文摘要 | Aerobic composting stands as a widely-adopted method for treating organic solid waste (OSW), simultaneously producing organic fertilizers and soil amendments. This biologically-driven biochemical reaction process, however, presents challenges due to its complex non-linear metabolism and the heterogeneous nature of the solid medium. These characteristics inherently limit the simulation accuracy and efficiency optimization in aerobic composting. Recently, significant efforts have been made to simulate and control composting process parameters, as well as predicting and optimizing composting product quality. Notably, the integration of machine learning (ML) in aerobic composting of organic waste has garnered considerable attention for its applicability and predictive capability in exploring the complex non-linear relationships of organic waste composting parameters. Despite numerous studies on ML applications in OSW composting, a systematic review of research findings in this field is lacking. This study offers a systematic overview of the application level, current status, and versatility of ML in OSW composting. It spans various aspects, such as compost maturity, environmental pollutants, nutrients, moisture, heat loss, and microbial metabolism. The survey reveals that ML-intervention predominantly focuses on compost maturity and environmental pollutants, followed by nutrients, moisture, heat loss, and microbial activity. The most commonly employed predictive models and optimization algorithms are artificial neural networks (47%) and genetic algorithms (10%). These demonstrate high prediction accuracy and maximize composting efficiency in the simulation and prediction of organic waste composting, alongside regulation of key parameters. Deep neural networks and ensemble learning models prove effective in achieving superior predictive performance by selecting feature variables in compost maturity and pollutant residue prediction of organic waste composting in a simpler and more objective manner. |
| WOS关键词 | NEURAL-NETWORK ; GENETIC ALGORITHM ; MANURE ; PARAMETERS ; KINETICS ; MODELS ; BIOAVAILABILITY ; HYDROQUINONE ; BIOSENSOR ; CATECHOL |
| 资助项目 | National Natural Science Foundation of China[52270143] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA28130300] ; Building Sci-Technological Resource to Promote Innovation and Entrepreneurship Upgradation of Small and Medium -Sized Enterprises[2020ZTSJ03] |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001194286800001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Building Sci-Technological Resource to Promote Innovation and Entrepreneurship Upgradation of Small and Medium -Sized Enterprises |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203893]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Hong-tao |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Li-ting,Hou, Jia-yi,Liu, Hong-tao. Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges[J]. WASTE MANAGEMENT,2024,178:155-167. |
| APA | Huang, Li-ting,Hou, Jia-yi,&Liu, Hong-tao.(2024).Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges.WASTE MANAGEMENT,178,155-167. |
| MLA | Huang, Li-ting,et al."Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges".WASTE MANAGEMENT 178(2024):155-167. |
入库方式： OAI收割
来源：地理科学与资源研究所
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