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RESUMO – The purpose of this research work is to develop an artificial neural network model that predicts the permeate flux of macromolecular solutions with guar and xanthan gums in ceramic membrane of nominal pore size of 0.2 μm and 0.4 μm to two different temperatures. The neural network has been trained through a selected set of experimental data already published. The experimental data were obtained for the concentration in turbulent flow. Few experimental series were considered to construct a database applied to neural model parameters that could be adjusted. The input variables of neural model were temperature, nominal pore size and microfiltration time. The results show that the neural model can be trained in a reasonable computational time and it is able to predict real values of the permeate flux.

NEURAL NETWORK MODEL FOR PREDICTION OF THE PERMEATE FLUX OF MACROMOLECULAR SOLUTIONS WITH GUAR AND XANTHAN GUMS IN CROSSFLOW MICROFILTRATION PROCESSING