Optimal Control of Total Chlorine and Free Ammonia Levels in a Water Transmission Pipeline Using Artificial Neural Networks and Genetic Algorithms

Abstract

In this study, a model predictive control (MPC) system is developed for the goldfield and agricultural water system (GAWS) east of Perth in Western Australia. As part of the study, four months’ water quality and hydraulic data of the system were collected for the development of the MPC system. Two artificial neural network (ANN) models are developed to model the relationships between the control variable, the ammonia dosing rate at the source, and the controlled variables, the total chlorine and free ammonia levels at a designated location (Goomalling pump station) in the network five days later. A two-step process based on both mutual information (MI) and partial mutual information (PMI) is used to select appropriate inputs for the total chlorine and free ammonia models. The total chlorine and free ammonia ANN models perform well, with validation Nash-Sutcliffe efficiencies of 0.84 and 0.62, respectively, and validation root mean square errors (RMSE) of 0.1320 and