Predicting the flux decline in milk cross-flow ceramic ultrafiltration by artificial neural networks Original Research Article

Fouling is complex phenomenon and an important drawback in the operation of membrane processes, thus its modeling involves scientific and commercial interest. In this research work, experimental data were collected by carrying out a sequence of cycles comprising both milk ultrafiltration through a 50 kDa tubular ceramic membrane and cleaning protocols with different agents. Then, it was developed an artificial neural network model that receives as inputs the operational cycle, the aggressivity of the cleaning and the filtration time and returns as output the permeate flux. Several training algorithms were tested and excellent fitting was obtained with the Levenberg–Marquardt one.