Paper Info
Title
The Study of Membrane Fouling Modeling Method Based on Wavelet Neural Network for Sewage Treatment Membrane Bioreactor
Abstract
The membrane bioreactor(MBR) is a new technology of sewage treatment combining the membrane with the bioreactor, but the membrane fouling is an important factor to limit the MBR further development. Considering the issues that the relationship between the membrane fouling and affecting factors is a complicated and nonlinear, a modeling method based on wavelet neural network is presented. We adopt a method of reduce the number of the wavelet basic function by analysis the sparsity property of sample data, and use the learning algorithm based on gradient descent to train network. The main parameters of affecting MBR membrane fouling are studied. With the ability of strong function approach and fast convergence of wavelet network, the modeling method can detect and assess the membrane fouling degree of MBR in real time by learning the membrane fouling information. The detection results show that this method is feasible and effective.
Year
DOI
Venue
2007
10.1109/ICNC.2007.750
ICNC
Keywords
Field
DocType
sewage treatment membrane bioreactor,membrane fouling degree,wavelet neural network,membrane fouling information,wavelet basic function,membrane bioreactor,strong function approach,membrane fouling,mbr membrane fouling,membrane fouling modeling method,modeling method,wavelet network,real time,sewage treatment,gradient descent,learning artificial intelligence,image classification,bioreactors,neural nets
Bioreactor,Convergence (routing),Membrane bioreactor,Gradient descent,Mathematical optimization,Biological system,Computer science,Membrane,Membrane fouling,Artificial neural network,Wavelet
Conference
Volume
ISSN
ISBN
2
2157-9555
0-7695-2875-9
Citations 
PageRank 
References 
0
0.34
4
Authors
4
Name
Order
Citations
PageRank
Meijuan Gao13212.32
Jingwen Tian23613.10
Lixin Zhao300.68
Kai Li433.30