Prediction of Water Quality and Alum Dose using Artificial Neural Network-Case Study of Surat

This study outlines the application of artificial neural network to improve prediction capability by investigating the effect of data sampling, network type and configuration as well as the inclusion of past data at the neural network input. To improve drinking water quality while reducing operating costs, many drinking water utilities are investing in advanced process control and automation technologies. The use of artificial intelligence technologies, specifically artificial neural networks, is increasing in the drinking water treatment industry as they allow for the development of robust nonlinear models of complex unit processes. This paper highlights the utility of artificial neural networks in water quality modeling as well as drinking water treatment process modeling and control through the presentation of a large-scale water treatment plants in Surat, Gujarat. The detailed objectives of this paper is to develop ANN models that are capable of predicting treated water quality parameters given raw water quality parameters and alum dose and to develop an ANN model that is capable of predicting the optimal alum dose given raw and treated water quality.