Predicting of membranes water fluxes using artificial neural network

摘要：

Water fluxes of polyetherimide (PEI) membranes are predicted by a neural network model which takes moment invariants of the cross-sectional scanning electronic microscope (SEM) images as inputs. The prediction includes two steps, the first is to construct the backpropagation neural network (BPNN) model, and the second is using the model to predict water flux of another group of PEI membranes. To construct the BPNN model, moment invariants of cross-sectional SEM images used as pattern features to represent the PEI membranes, are calculated and normalized. Meanwhile, water fluxes of the PEI membranes are determined by experiments. These moment invariants together with the water fluxes are utilized to train the BPNN model, and to obtain the best network architecture. When another group of PEI membranes moment invariants are inputted to the BPNN model, their water flux are predicted. Compared the experimental water flux with the predicted results, the standard deviation is less than 6.7%.

关键词： membrane flux neural network moment invariant BPNN

作者: Ming Tan Gaohong He Yuanfa Liu Xudong Liu Wei Zhao

作者单位: State Key Laboratory of Fine Chemicals, R&D Center of Membrane Science and Technology, Dalian University of Technology, 158 Zhongshan Road, Dalian, 116012,China

会议类型: 国际会议

会议名称: The 12th Asian Pacific Confederation of Chemical Engineering Congress(第十二届亚太化工联盟大会暨化工展览会)

会议地点: 大连

会议语种:英文

在线出版日期: 2008-08-04（万方平台首次上网日期，不代表论文的发表时间）