吸附
污染物
可解释性
过程(计算)
水污染物
工作流程
计算机科学
工艺工程
生化工程
环境科学
化学
环境化学
工程类
人工智能
有机化学
操作系统
数据库
作者
Wentao Zhang,Wenguang Huang,Jie Tan,Dawei Huang,Jun Ma,Bingdang Wu
出处
期刊:Chemosphere
[Elsevier]
日期:2022-10-29
卷期号:311: 137044-137044
被引量:45
标识
DOI:10.1016/j.chemosphere.2022.137044
摘要
It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.
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