Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip

A rapidly growing technique for producing new water is desalination of seawater and brackish water. In the Gaza Strip the maximum amount of the drinking water is produced through small private desalination facilities. The present paper is concerned with using artificial neural network (ANN) technique to forecast reverse osmosis desalination plant's performance in the Gaza Strip through predicting the next week values of total dissolved solids (TDS) and permeate flowrate of the product water. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks were trained and developed with reference to feed water parameters including: pressure, pH and conductivity to predict permeate flowrate next week values. MLP and RBF neural networks were used for predicting the next week TDS concentrations. Both networks are trained and developed with reference to product water quality variables including: water temperature, pH, conductivity and pressure. The prediction results showed that both types of neural networks are highly satisfactory for predicting TDS level in the product water quality and satisfactory for predicting permeate flowrate. Results of both developed networks were compared with the statistical model and found that ANN predictions are better than the conventional methods. (C) 2015 Elsevier B.V. All rights reserved.