A Review on Applications of Artificial Intelligence in Wastewater Treatment

Yi Wang, Yuhan Cheng, He Liu, Qing Guo, Chuanjun Dai, Min Zhao () and Dezhao Liu ()
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Yi Wang: Institute of Agri-Biological Environmental Engineering, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Yuhan Cheng: School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
He Liu: School of Mathematics and Physics, Wenzhou University, Wenzhou 325035, China
Qing Guo: School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
Chuanjun Dai: School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
Min Zhao: School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
Dezhao Liu: Institute of Agri-Biological Environmental Engineering, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Sustainability, 2023, vol. 15, issue 18, 1-28

Abstract: In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

Keywords: artificial intelligence; wastewater treatment; machine learning; artificial neural network; search algorithm; water quality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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