废水
污水处理
过程(计算)
生化工程
流出物
污染物
计算机科学
人工神经网络
环境科学
人工智能
工程类
环境工程
生态学
生物
操作系统
作者
Shubo Zhang,Ying Jia,Wenkang Chen,Jinfeng Wang,Yanru Wang,Hongqiang Ren
出处
期刊:Chemosphere
[Elsevier]
日期:2023-09-01
卷期号:336: 139163-139163
被引量:5
标识
DOI:10.1016/j.chemosphere.2023.139163
摘要
Wastewater treatment is a complex process that involves many uncertainties, leading to fluctuations in effluent quality and costs, and environmental risks. Artificial intelligence (AI) can handle complex nonlinear problems and has become a powerful tool for exploring and managing wastewater treatment systems. This study provides a summary of the current status and trends in AI research as applied to wastewater treatment, based on published papers and patents. Our results indicate that, at present, AI is primarily used to evaluate removal of pollutants (conventional, typical, and emerging contaminants), optimize models and process parameters, and control membrane fouling. Future research will likely continue to focus on removal of phosphorus, organic pollutants, and emerging contaminants. Moreover, analyzing microbial community dynamics and achieving multi-objective optimization are promising directions of research. The knowledge map shows that there may be future technological innovation related to predicting water quality under specific conditions, integrating AI with other information technologies and utilizing image-based AI and other algorithms in wastewater treatment. In addition, we briefly review development of artificial neural networks (ANNs) and explore the evolutionary path of AI in wastewater treatment. Our findings provide valuable insights into potential opportunities and challenges for researchers applying AI to wastewater treatment.
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