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.