Abstract
The neural network theory was used to dynamically model membrane fouling for a raw cane sugar syrup feed stream. The use of neural networks enabled us to integrate the effects of hydrodynamic conditions on the time evolution of the total hydraulic resistance of the membrane under constant temperature and feed stream concentration. The results obtained satisfactorily model the effects of both constant and variable transmembrane pressure and crossflow velocity as the filtration was followed through time. The effects of the hidden network structure as well as the scatter of data on the quality of modeling are discussed in this paper.