Paper Info
Title
Fuzzy Neural Network-Based Model Predictive Control for Dissolved Oxygen Concentration of WWTPs
Abstract
Dissolved oxygen (DO) concentration is a key variable in the operation of wastewater treatment processes (WWTPs). In this paper, an adaptive fuzzy neural network-based model predictive control (AFNN-MPC) is proposed for the control problem of DO concentration. The main contributions of AFNN-MPC are threefolds: First, an AFNN, based on a novel learning method with adaptive learning rate, is designed to model the unknown nonlinearities of WWTPs with high predicting performance. Second, a gradient method is used to solve the optimal control problem of AFNN-MPC to reduce the computational cost. Third, the convergence of AFNN, as well as the stability analysis of AFNN-MPC, has been given in detail. Finally, the proposed AFNN-MPC is applied to the benchmark simulation model No. 2. The promising results indicate that the proposed AFNN-MPC is a suitable solution to control DO concentration. Moreover, the comprehensive experiments clearly show the superiority and efficacy of the proposed AFNN-MPC.
Year
DOI
Venue
2019
10.1007/s40815-019-00644-8
International Journal of Fuzzy Systems
Keywords
Field
DocType
Dissolved oxygen concentration, Wastewater treatment processes, Adaptive fuzzy neural network, Model predictive control, Benchmark simulation model No. 2
Convergence (routing),Gradient method,Mathematical optimization,Optimal control,Control theory,Oxygen saturation,Model predictive control,Adaptive learning rate,Artificial neural network,Mathematics
Journal
Volume
Issue
ISSN
21
5
1562-2479
Citations 
PageRank 
References 
1
0.36
0
Authors
3
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
zheng liu226721.86
Jun-Fei Qiao379874.56