MODELING OF ULTRAFILTRATION FOULING BY NEURAL-NETWORK

Optimisation of ultrafiltration pilot plants requires a better knowled ge of membrane fouling. Zn the field of drinking water production, phe nomena involved in fouling are very complex and interdependent because of the numerous compounds contained in raw waters. As no knowledge mo del is available for this application, a statistical modelling tool ca lled neural network is used in this paper to predict the total hydraul ic resistance at the end of a filtration cycle and after next backwash , using some parameters concerning water quality (turbidity and temper ature) and operating conditions, for a given experimental site. Differ ent network structures have been evaluated, using information concerni ng the current filtration cycle and the previous cycle. Some of them a llow a prediction of resistance with a very good accuracy. They take i nto account as network inlets the permeate flow rate, pressure and wat er turbidity, and are able to model the effects of reversible fouling on resistance.