文献类型：会议论文

英文题名：Membrane Fouling Modeling of Sewage Treatment Membrane Bioreactor Based on Radial Basic Function Neural Network

作者：Tian, Jingwen[1,2];Gao, Meijuan[1,2];Zhou, Shiru[1];Zhang, Yu[2]

通讯作者：Tian, JW[1]

机构：[1]Beijing Union Univ, Dept Automat Control, Beijing, Peoples R China;[2]Beijing Union Univ, Sch Informat Sci, Beijing, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Dept Automat Control, Beijing, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：IEEE International Conference on Automation and Logistics

会议日期：SEP 01-03, 2008

会议地点：Qingdao, PEOPLES R CHINA

语种：英文

外文关键词：Radial basic function network; Membrane fouling; Membrane bioreactor; Modeling

摘要：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.