Predicting salt rejections at nanofiltration membranes using artificial neural networks

An artificial neural network (ANN) has been used to predict the rejections of single salts (NaCl, Na2SO4, MgCl2 and MgSO4) and mixtures of these salts at a nanofiltration membrane. Such rejections show complex non-linear dependencies on salt concentration, mixture composition, pH and applied pressure and provide a demanding test of the application of ANN analysis to membrane processes. A single optimized network was used for all predictions, the network having the ability to switch on/off its internal parts depending on the process solution. A qualitative physical understanding of the process was used in choosing the appropriate input variables. The predictions have been compared to pilot plant rejection data obtained with a spiral-wound membrane. The overall agreement between ANN predictions and experimental data was very good for both single salts and mixtures. In practical circumstances, the ANN approach to nanofiltration has the advantage of only requiring simple and readily available inputs and a minimum understanding of the complex phenomena controlling rejection.