Modeling of an RO water desalination unit using neural networks

In this paper, a feedforward neural network (NN) model is developed to predict the performance of a reverse osmosis (RO) experimental setup, which uses a FilmTec SW30 membrane. Sixty-three experimental data were generated for training and testing the network. The considered ranges of operating conditions were chosen so as to include those encountered in a large number of the worldwide brackish water and seawater RO plants. The NN was fed with three inputs: the feed pressure, temperature and salt concentration to predict the water permeate rate. The fast Levenberg–Marquardt (LM) optimization technique was employed for training the NN. The network learned the input–output mappings with accuracy for interpolation cases, but not for extrapolation.