Artificial neural networks for water quality soft-sensing in wastewater treatment: a review
作者:Gongming Wang, Qing-Shan Jia, MengChu Zhou, Jing Bi, Junfei Qiao, Abdullah Abusorrah
摘要
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.
论文关键词:Soft-sensing model, Wastewater treatment process (WWTP), Artificial neural network, Deep belief network, Machine learning, Soft-sensing example
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论文官网地址:https://doi.org/10.1007/s10462-021-10038-8