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A Review on Applications of Artificial Intelligence in Wastewater Treatment

Author

Listed:
  • Yi Wang

    (Institute of Agri-Biological Environmental Engineering, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
    Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
    School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
    National & Local Joint Engineering Research Center for Ecological Treatment Technology of Urban Water Pollution, Wenzhou University, Wenzhou 325035, China)

  • Yuhan Cheng

    (School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
    Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310030, 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
    Environmental Engineering Program, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada)

  • Chuanjun Dai

    (School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
    National & Local Joint Engineering Research Center for Ecological Treatment Technology of Urban Water Pollution, Wenzhou University, Wenzhou 325035, China)

  • Min Zhao

    (School of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
    National & Local Joint Engineering Research Center for Ecological Treatment Technology of Urban Water Pollution, 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
    Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China)

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.

Suggested Citation

  • Yi Wang & Yuhan Cheng & He Liu & Qing Guo & Chuanjun Dai & Min Zhao & Dezhao Liu, 2023. "A Review on Applications of Artificial Intelligence in Wastewater Treatment," Sustainability, MDPI, vol. 15(18), pages 1-28, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13557-:d:1237491
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    References listed on IDEAS

    as
    1. Siva Rama Krishnan & M. K. Nallakaruppan & Rajeswari Chengoden & Srinivas Koppu & M. Iyapparaja & Jayakumar Sadhasivam & Sankaran Sethuraman, 2022. "Smart Water Resource Management Using Artificial Intelligence—A Review," Sustainability, MDPI, vol. 14(20), pages 1-28, October.
    2. Wirginia Tomczak & Marek Gryta, 2022. "Energy-Efficient AnMBRs Technology for Treatment of Wastewaters: A Review," Energies, MDPI, vol. 15(14), pages 1-40, July.
    3. Héctor Rodríguez-Rángel & Dulce María Arias & Luis Alberto Morales-Rosales & Victor Gonzalez-Huitron & Mario Valenzuela Partida & Joan García, 2022. "Machine Learning Methods Modeling Carbohydrate-Enriched Cyanobacteria Biomass Production in Wastewater Treatment Systems," Energies, MDPI, vol. 15(7), pages 1-18, March.
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    Cited by:

    1. Binglin Li & Hao Xu & Yufeng Lian & Pai Li & Yong Shao & Chunyu Tan, 2023. "An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters," Sustainability, MDPI, vol. 15(24), pages 1-18, December.

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