SIMULATION OF MEMBRANE SEPARATION BY NEURAL NETWORKS

A method for the simulation of membrane processes by using neural networks was developed. The neural network model is used for obtaining an estimation of permeate flux and rejection over the entire range of the process variables, i.e, pressure, concentration of solute, temperature and superficial flow velocity. Permeate flux and rejection are dependent on the process variables. This dependence has to be known in the flowsheet simulation of membrane processes. Traditionally it has been described by experimental polynomial correlations or by equations based on a mass transfer model. Neural networks offer the advantage of being easy to use. Furthermore, the computing time in the design of a membrane separation plant is shorter as compared with the calculation based on mass transfer models.