Paper Info
Title
Data-Driven Intelligent Warning Method for Membrane Fouling
Abstract
Membrane fouling has become a serious issue in membrane bioreactor (MBR) and may destroy the operation of the wastewater treatment process (WWTP). The goal of this article is to design a data-driven intelligent warning method for warning the future events of membrane fouling in MBR. The main novelties of the proposed method are threefold. First, a soft-computing model, based on the recurrent fuzzy neural network (RFNN), was proposed to identify the real-time values of membrane permeability. Second, a multistep prediction strategy was designed to predict the multiple outputs of membrane permeability accurately by decreasing the error accumulation over the predictive horizon. Third, a warning detection algorithm, using the state comprehensive evaluation (SCE) method, was developed to evaluate the pollution levels of MBR. Finally, the proposed method was inserted into a warning system to complete the predicting and warning missions and further tested in the real plants to evaluate its efficiency and effectiveness. Experimental results have verified the benefits of the proposed method.
Year
DOI
Venue
2021
10.1109/TNNLS.2020.3041293
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Data-driven,intelligent warning method,membrane fouling,recurrent fuzzy neural network (RFNN),state comprehensive evaluation (SCE)
Journal
32
Issue
ISSN
Citations 
8
2162-237X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Xiao-Long Wu1302.77
Hong-Gui Han247639.06
Jun-Fei Qiao379874.56