Dynamic prediction of milk ultrafiltration performance: A neural network approach

Neural network models were tested in connection with the dynamic prediction of permeate flux ( J P ), total hydraulic resistance ( R T ) and the solutes rejection for the crossflow ultrafiltration of milk at different transmembrane pressure (TMP) and temperature ( T). This process has complex non-linear dependencies on the operating conditions. Thus it provides demanding test of the neural network approach to the process variables prediction. Two neural network models with single hidden layer were constructed to predict the time dependent rate of J P / R T and rejections from a limited number of training data. The modelling results showed that there is an excellent agreement between the experimental data and predicted values, with average errors less than 1%. The experimental results showed that the R T and solutes rejection (except for protein) increased greatly with time at each value of TMP and T, whereas the J P decreased significantly for the same conditions. Increasing TMP at constant T led to an increase in the J P , R T and solutes rejection, but increasing T at constant TMP had no significant effect on the J P , R T and rejection of components.