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Artificial neural networks for water quality soft-sensing in wastewater treatment: a review

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Abstract

This paper aims to present a comprehensive survey on water quality soft-sensing of a wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We mainly present problem formulation of water quality soft-sensing, common soft-sensing models, practical soft-sensing examples and discussion on the performance of soft-sensing models. In details, problem formulation includes characteristic analysis and modeling principle of water quality soft-sensing. The common soft-sensing models mainly include a back-propagation neural network, radial basis function neural network, fuzzy neural network (FNN), echo state network (ESN), growing deep belief network and deep belief network with event-triggered learning (DBN-EL). They are compared in terms of accuracy, efficiency and computational complexity with partial-least-square-regression DBN (PLSR-DBN), growing ESN, sparse deep belief FNN, self-organizing DBN, wavelet-ANN and self-organizing cascade neural network (SCNN). In addition, this paper generally discusses and explains what factors affect the accuracy of the ANNs-based soft-sensing models. Finally, this paper points out several challenges in soft-sensing models of WWTP, which may be helpful for researchers and practitioner to explore the future solutions for their particular applications.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 62003185, 62073182, 61890930 and 61890935), in part by the National Science and Technology Major Project (2018ZX07111005). No conflict of interest exits in this manuscript and it has been approved by all authors for publication.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Gongming Wang, Jing Bi & Junfei Qiao

  2. Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, Beijing, 100084, China

    Qing-Shan Jia

  3. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    MengChu Zhou

  4. Department of Electrical and Computer Engineering, Faculty of Engineering, and Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah, 21481, Saudi Arabia

    Abdullah Abusorrah

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  1. Gongming Wang
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  2. Qing-Shan Jia
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  4. Jing Bi
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Correspondence to Gongming Wang.

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Wang, G., Jia, QS., Zhou, M. et al. Artificial neural networks for water quality soft-sensing in wastewater treatment: a review. Artif Intell Rev 55, 565–587 (2022). https://doi.org/10.1007/s10462-021-10038-8

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