Title
Wastewater Treatment Control Method Based On A Rule Adaptive Recurrent Fuzzy Neural Network
Abstract
Purpose - The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.Design/methodology/approach - A control strategy based on rule adaptive recurrent neural network (RARFNN) is proposed in this paper to control the dissolved oxygen (DO) concentration and nitrate nitrogen (SNo) concentration. The structure of the RARFNN is self-organized by a rule adaptive algorithm, and the rule adaptive algorithm considers the overall information processing ability of neural network. Furthermore, a stability analysis method is given to prove the convergence of the proposed RARFNN.Findings - By application in the control problem of wastewater treatment process (WWTP), results show that the proposed control method achieves better performance compared to other methods.Originality/value - The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP. The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations. And, the rule adaptive mechanism considers the overall information processing ability judgment of the neural network, which can ensure that the neural network contains the information of the biochemical reactions.
Year
DOI
Venue
2017
10.1108/IJICC-12-2016-0069
INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS
Keywords
Field
DocType
Information processing ability, Recurrent fuzzy neural network, Rule adaptive, Wastewater treatment
Convergence (routing),Mathematical optimization,Information processing,Computer science,Recurrent neural network,Biochemical reactions,Sewage treatment,Artificial intelligence,Adaptive algorithm,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
10
2
1756-378X
Citations 
PageRank 
References 
0
0.34
13
Authors
4
Name
Order
Citations
PageRank
Jun-Fei Qiao179874.56
Gai-Tang Han201.01
Hong-Gui Han347639.06
Wei Chai400.34