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 , respectively. A real-number coded genetic algorithm is then used to find the optimal ammonia dosing rate to achieve the target total chlorine and free ammonia levels at the modeled location. The results demonstrate that the developed MPC system can control the total chlorine and free ammonia levels at Goomalling pump station to be close to their target values by adjusting the ammonia dosing rates at Mundaring pump stations. The errors in the MPC system are mainly due to the relatively weak relationship between the control and controlled variables for this particular system.
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Acknowledgments
The authors would ike to acknowledge Water Research Australia for its financial support for this project. The authors would also like to thank Associate Professor David Davey and Dr. Stanley McLeod from the University of South Australia for the development and maintenance of the free ammonia analyzer, Mr. Ralph Henderson, Mr. Brett Kerenyi, and Mr. Ross Taylor from Water Corporation for their assistance in data collection and maintenance of the analyzer on-site, and Dr. Chris Chow of SA Water for his helpful advice during the project.
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Journal of Water Resources Planning and Management
Volume 141 • Issue 7 • July 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Nov 8, 2013
Accepted: Aug 14, 2014
Published online: Sep 11, 2014
Discussion open until: Feb 11, 2015
Published in print: Jul 1, 2015
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