Membrane Fouling Modeling of Sewage Treatment Membrane Bioreactor Based on Radial Basic Function Neural Network

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 membrane fouling modeling method based on radial basic function neural network (RBFNN) is presented in this paper. We construct the structure of RBFNN that used for membrane fouling, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. The main parameters of affecting MBR membrane fouling are studied. With the ability of strong function approach and fast convergence of RBFNN, the modeling method can detect and assess the membrane fouling degree of MBR in real time by learning the membrane fouling information. The experimental results show that this method is feasible and effective.