Author(s): Jahangiri M and Aminian A
Textile industries represent an important environmental problem due to their high water consumption. In order to economically water consumption, wastewater treatment is necessary for water reuse in the textile industries. Predicting the performance of nanofiltration membrane, as an effective separation process, is necessary for the design and depiction of process. Prediction of the rejection of degradable components is especially important. In this work, an Artificial Neural Network (ANN) is used to predict the rejection of Chemical Oxygen Demand (COD) in a cross-flow nanofiltration membrane at textile wastewater effluent stream. Rejections are predicted as a function of feed pressure and permeate flux with cross flow velocity. ANN predictions of the COD rejection are compared with experimental results obtained using two different nanofiltration membranes (NF-90 and DK-5). The results show a good agreement between experimental data and the output from the neural network simulation.
