Development of nanofiltration membrane separation prediction system for binary salt solutions

The previous studies on nanofiltration (NF) mainly focused on the work to update the existing predictive models to enhance its application in order to optimize the separation prediction. There is still lack of research which successfully creates a user-friendly system for separation process prediction optimization. In this study, a MATLAB (R)-based NF prediction system (NF-BIN) utilizing Donnan Steric Pore model with application of m-file programming and graphical user interface was developed specifically for binary salt solutions treatment. Prior to the prediction, locally fabricated polyethersulfone membranes with three different polymer concentrations, 19, 21, and 23%, were characterized in terms of pore radius, r(p) ratio of membrane thickness to porosity, x/A(k) and effective charge density, X-d using uncharged, and charged solutes rejection data. Further the rejection prediction performance was carried out to predict the percentage contribution of ion transport mechanism of three transport modes: diffusion, electromigration, and convection as described in Extended Nernst-Planck equation. The results obtained from this study indicated that NF/BIN has a good potential as an ideal predictor of NF membrane separation behavior.