The ability of neural network approach was investigated for the dynamic simulation of crossflow milk ultrafiltration. It aims to model the permeate flux and total hydraulic resistance as a function of pH, fat percent and operation time. Feed forward perceptron networks with a single hidden layer were used to simulate the time-dependent rate of ultrafiltration from a few experimental data. The effect of the number of training points, the number of hidden neurons and training data arrangements on the accuracy of simulation were studied in this work. The results showed that the quality of simulation could be improved using appropriate selection of training points and small network. The best network was able to accurately capture the non-linear dynamics of milk ultrafiltration, so that the agreement between the actual data and simulated values was excellent with maximum and average errors less than 3.61% and 1.06%, respectively.

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