Neural models for monitoring the transmembrane flux in the vinasse clarification process by crossflow microfiltration

Abstract

Artificial Neural Networks (ANN) were used for estimating the transmembrane flux in a crossflow microfiltration process with ceramic tubular membranes to clarify the vinasse. The prevision was accomplished through the training of ANN feedforward using the experimental database generated in the work of Trevisoli (2010). The results showed a good correlation between the estimated data and the experimental data of transmembrane flux. For the microfiltration process with the membrane nominal pore size of 0.8 μm, the test subset presented maximum percentage error of 5.21% and average percentage error of 1.62%. For the membrane nominal pore size of 1.2 μm, the test subset had maximum percentage error of 28.51% and average percentage error of 4.66%. Therefore, it is feasible to use the ANN technique to estimate future data, helping to study membranes in microfiltration processes.