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

Document Type

Article

Publication Date

1-1-2022

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.

Identifier

85108813078 (Scopus)

Publication Title

Artificial Intelligence Review

External Full Text Location

https://doi.org/10.1007/s10462-021-10038-8

e-ISSN

15737462

ISSN

02692821

First Page

565

Last Page

587

Issue

1

Volume

55

Grant

61890930

Fund Ref

National Natural Science Foundation of China

This document is currently not available here.

Share

COinS