Application of soft computing in water treatment plant and water distribution network

Abstract

Analysis of traditional water distribution network (WDN) is more time-consuming and less effective to predict the problem related to water supply systems such as water quality, coagulant dose, and residual chlorine in developing countries. In the present paper water quality neural network, coagulation dose neural network, and residual neural network model were implemented. The performance of the Cascade Feed Forward Neural Network (CFFNN) and Feedforward neural network (FFNN) was excellent for the prediction of water quality parameters and residual chlorine respectively during the training and testing period. CFFNN water quality model (27-30-27) with R = 0.989 produced an excellent prediction of outlet water quality parameters. In coagulant dose modelling, CFFNN (2-40-1) yielded a good prediction with R = 0.947 for a broad range of turbidities as compared to other models. Similarly in residual chlorine modelling, FFNN (2-25-1) delivered the best prediction with R = 0.988 as compared to other models.