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

Gongming Wang, Qing Shan Jia, Meng Chu Zhou, Jing Bi, Junfei Qiao, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
JournalArtificial Intelligence Review
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

Keywords

  • Artificial neural network
  • Deep belief network
  • Machine learning
  • Soft-sensing example
  • Soft-sensing model
  • Wastewater treatment process (WWTP)

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