Determination of The Salt Rejection Efficiency by Nanofiltration Membranes Using Neural Networks

In this paper, a feedforward neural network (NN) model is used to determination the salt rejection efficiency of a Nanofiltration (NF) experimental setup, which uses a DS5 nanofiltration membrane. Experimental data were collected from literature. Two hundred thirty eight experimental data were used for training and testing the network. The NN was fed with five inputs: the feed pressure, pH, salt concentration, dye concentration and cross flow velocity to determination the salt rejection efficiency. The Scaled Conjuge Gradient Algorithm (SCG) was used as optimization algorithm for training the NN. The best network configuration was set as 5-9-1 with trying and testing. The model will determine the salt rejection efficiency of nanofiltration membranes based on input and output parameters. The network was trained with a hundred seventy eight experimental data and tested with sixty experimental data. The mean absolute percentage error method was used to evaluate performance of NN. The mean absolute percentage error of training and testing results were 4.22 and 3.84 respectively. It is shown that the agreement between NN predictions and experimental data was very good.