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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 673156, 8 pages
Research Article

The Application and Research of the GA-BP Neural Network Algorithm in the MBR Membrane Fouling

1School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China
2School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin 300387, China

Received 4 December 2013; Accepted 24 January 2014; Published 6 March 2014

Academic Editor: Rudong Chen

Copyright © 2014 Chunqing Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


It is one of the important issues in the field of today's sewage treatment of researching the MBR membrane flux prediction for membrane fouling. Firstly this paper used the principal component analysis method to achieve dimensionality and correlation of input variables and obtained the three major factors affecting membrane fouling most obvious: MLSS, total resistance, and operating pressure. Then it used the BP neural network to establish the system model of the MBR intelligent simulation, the relationship between three parameters, and membrane flux characterization of the degree of membrane fouling, because the BP neural network has slow training speed, is sensitive to the initial weights and the threshold, is easy to fall into local minimum points, and so on. So this paper used genetic algorithm to optimize the initial weights and the threshold of BP neural network and established the membrane fouling prediction model based on GA-BP network. As this research had shown, under the same conditions, the BP network model optimized by GA of MBR membrane fouling is better than that not optimized for prediction effect of membrane flux. It demonstrates that the GA-BP network model of MBR membrane fouling is more suitable for simulation of MBR membrane fouling process, comparing with the BP network.