Predicting the Flux of BSA Solutions in the Dead-end Microfiltration

WANG Zhan; XI Xue-jie; YAO Jin-miao; SONG Yin; ZHAO Shan-shan; WANG Xiu-yan; YANG Li-ying; LI Wen-juan; AN Kun; ZHANG Jing; CHU Jin-shu

Journal of Beijing University of Technology > 2010 > 36(2): 235-239,267.

WANG Zhan, XI Xue-jie, YAO Jin-miao, SONG Yin, ZHAO Shan-shan, WANG Xiu-yan, YANG Li-ying, LI Wen-juan, AN Kun, ZHANG Jing, CHU Jin-shu. Predicting the Flux of BSA Solutions in the Dead-end Microfiltration[J]. Journal of Beijing University of Technology, 2010, 36(2): 235-239,267.

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Predicting the Flux of BSA Solutions in the Dead-end Microfiltration

1. College of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100124, China;
2. Beijing Fluid Filtration & Separation Technology Research Center, Beijing 101312, China

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Received Date: February 26, 2008
Available Online: December 29, 2022

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In order to predict the flux of BSA solutions under the different operating conditions (transmembrane pressure, feed concentration and temperature) in the dead-end microfiltration, the training epochs, correlative coefficient and relative absolute error were used as three predictive criterions, and the configurations of the developed three layers BP and RBF neural network were optimized by changing the interior parameters of neural networks.The result showed that, in the experimental rang, an optimal configuration of the available BP neural network is 3-9-1, the number of hidden neurons is 9, the learning rate is 0.05, the learning function is traingdx, the transfer function is logsig.By using the BP neural network, the obtained average relative absolute error and correlative coefficient is 2.37%, 0.9960, respectively.By using the RBF neural network, the designable function of the network for an optimal configuration of the available RBF neural network is newrbe, and its spread is 400.The obtained average relative absolute error and correlative coefficient is 4.83%, 0.9870, respectively.Therefore an BP neural network is much better than a RBF neural network in the study of predicting the flux of BSA solutions in the dead-end microfiltration.
- dead-end microfiltration,
- flux,
- BP neural network,
- RBF neural network

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