污染物
粒子群优化
人工神经网络
遗传算法
计算机科学
人工智能
生化工程
环境污染
环境科学
机器学习
工程类
生态学
生物
环境保护
作者
Mingyi Fan,Jiwei Hu,Rensheng Cao,Wenqian Ruan,Xionghui Wei
出处
期刊:Chemosphere
[Elsevier]
日期:2018-02-20
卷期号:200: 330-343
被引量:219
标识
DOI:10.1016/j.chemosphere.2018.02.111
摘要
Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection.
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