Simulation, prediction and optimization of typical heavy metals immobilization in swine manure composting by using machine learning models and genetic algorithm
文献类型：期刊论文
| 作者 | Guo, Hao-Nan2,3; Liu, Hong-Tao2,4; Wu, Shubiao1 |
| 刊名 | JOURNAL OF ENVIRONMENTAL MANAGEMENT
 |
| 出版日期 | 2022-12-01 |
| 卷号 | 323页码:9 |
| ISSN号 | 0301-4797 |
| 关键词 | Composting Heavy metal Risk reduction Machine learning Genetic algorithm |
| DOI | 10.1016/j.jenvman.2022.116266 |
| 通讯作者 | Liu, Hong-Tao(liuht@igsnrr.ac.cn) |
| 英文摘要 | Machine learning (ML) is a novel method of data analysis with potential to overcome limitations of traditional composting experiments. In this study, four ML models (multi-layer perceptron regression, support vector regression, decision tree regression, and gradient boosting regression) were integrated with genetic algorithm to predict and optimize heavy metal immobilization during composting. Gradient boosting regression performed best among the four models for predicting both heavy metal bioavailability variations and immobilization. Gradient boosting regression-based feature importance analysis revealed that the heavy metal initial bioavailoability factor, total phosphorus, and composting duration were the determinant factors for heavy metal bioavailability variations (together contributing >75%). After genetic algorithm optimization, the maximum immobilization rates of Cu, Zn, Cd, As, and Cr were 79.53, 31.30, 14.91, 46.25, and 66.27%, respectively, suoperior to over 90% of the measured data. These findings demonstrate the potential application of ML to riskcontrol for heavy metals in livestock manure composting. |
| WOS关键词 | SOLID-WASTE GENERATION ; ARTIFICIAL NEURAL-NETWORK ; BIOGAS PRODUCTION ; BIOAVAILABILITY ; CU ; SPECIATION ; ZN ; PASSIVATION ; PARAMETERS ; MANAGEMENT |
| 资助项目 | National Natural Science Foundation of China[52270143] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28130300] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23050103] ; National Key R&D Program of China[2018YFD0500205] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:001043916100005 |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Key R&D Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/196644]  |
| 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.Aarhus Univ, Dept Agroecol, Blichers Alle 20, DK-8830 Tjele, Denmark 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Hao-Nan,Liu, Hong-Tao,Wu, Shubiao. Simulation, prediction and optimization of typical heavy metals immobilization in swine manure composting by using machine learning models and genetic algorithm[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT,2022,323:9. |
| APA | Guo, Hao-Nan,Liu, Hong-Tao,&Wu, Shubiao.(2022).Simulation, prediction and optimization of typical heavy metals immobilization in swine manure composting by using machine learning models and genetic algorithm.JOURNAL OF ENVIRONMENTAL MANAGEMENT,323,9. |
| MLA | Guo, Hao-Nan,et al."Simulation, prediction and optimization of typical heavy metals immobilization in swine manure composting by using machine learning models and genetic algorithm".JOURNAL OF ENVIRONMENTAL MANAGEMENT 323(2022):9. |
入库方式： OAI收割
来源：地理科学与资源研究所
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