TY - JOUR
T1 - Artificial neural networks for water quality soft-sensing in wastewater treatment
T2 - a review
AU - Wang, Gongming
AU - Jia, Qing Shan
AU - Zhou, Meng Chu
AU - Bi, Jing
AU - Qiao, Junfei
AU - Abusorrah, Abdullah
N1 - Funding Information:
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.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature B.V.
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Deep belief network
KW - Machine learning
KW - Soft-sensing example
KW - Soft-sensing model
KW - Wastewater treatment process (WWTP)
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U2 - 10.1007/s10462-021-10038-8
DO - 10.1007/s10462-021-10038-8
M3 - Article
AN - SCOPUS:85108813078
SN - 0269-2821
VL - 55
SP - 565
EP - 587
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 1
ER -