Some mathematical models have properly predicted the RO membrane performances. The equations of these models which were usually complex and time consuming were solved algebraically and numerically. The modified surface force-pore flow model is one of the best models which has predicted the RO membrane performances, for example separation factor (f), pure solvent flux (N P) and total flux (N T), better than the others. In this study, these performances were computed by use of an artificial neural networks technique by applying the parameters of this model and the physical properties of the membrane. A back-propagation feed-forward network with three layers including 9 neurons in the first layer and one neuron in the output layer was used. Minimum error was found with 20 neurons in the second layer, by trial and error. Some experimental data were used for simulating the network. The network was trained in two subsequent steps including feed-forward and error back-propagation. The datasets were randomly divided into three parts: 70 % of them were applied for training, 15 % were used for validating, and the remaining 15 % were applied for testing. The predicted values of the network were compared with experimental data existing for RO membrane performances (f, N P, and N T). A mean square error less than 0.0007 was achieved and a correlation coefficient with more than 0.99 was derived for the test datasets.
Research on Chemical Intermediates – Springer Journals
Published: Oct 16, 2012
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