Neural network modeling for separation of bentonite in tubular ceramic membranes

Abstract

The neural network model is used for obtaining an estimation of permeate flux and rejection over the entire range of process variables. This approach has been extended in this study and applied to the prediction of flux sustainability and membrane efficiency of ceramic tubular membranes. Experimental results involving the use of turbulence promoters and the empty membrane filtration have been obtained and are directly compared to the predicted values from the black box model. Flux sustainability and membrane efficiency are dependent on feed temperature, system pressure, feed concentration and crossflow velocity. Neural networks also offer the added advantage of being quite straightforward in its application. The possibility of using BPNN (back-propagation network) to accurately predict variable effects on flux sustainability is included. Turbulence promoters were used experimentally to significantly enhance membrane efficiency and flux sustainability during microfiltration of dilute bentonite suspensions. Artificial neural networks can predict very accurately real system behaviour with relative errors reaching at most 5%. In order to obtain the data set necessary to train the different networks, three concentrations, three system pressures, three feed temperatures and one feed flowrate were tested in several operating conditions.