文献类型：会议论文

英文题名：The study of membrane fouling modeling method based on support vector machine for sewage treatment membrane bioreactor

作者：Gao, Meijuan[1,2];Tian, Jingwen[1,2];Li, Jin[2]

第一作者：Gao, Meijuan;高美娟

通讯作者：Gao, MJ[1]

机构：[1]Beijing Union Univ, Dept Automat Control, Beijing, Peoples R China;[2]Beijing Univ Chem Technol, 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：2nd IEEE Conference on Industrial Electronics and Applications (ICIEA 2007)

会议日期：MAY 23-25, 2007

会议地点：Harbin, PEOPLES R CHINA

语种：英文

外文关键词：Iterative methods - Parameter estimation - Real time systems - Sewage treatment - Support vector machines

摘要：The membrane bioreactor(MBR) ia 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 support vector machine(SVM) is presented in this paper. The main parameters of affecting MBR membrane fouling is studied. The SVM network structure for membrane fouling is established. Moreover, we propose a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters, thereby enhancing the converging speed and the predicting accuracy. With the ability of strong self-learning and well generalization of SVM, the modeling method can detect and assesse 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.