Optimization Research of Parallel Pump System for Improving Energy Efficiency
Publication: Journal of Water Resources Planning and Management
Volume 141, Issue 8
Abstract
In applications where demand on a water supply system changes frequently and widely, the operating conditions of pumps always deviate from the design conditions, and this error leads to poor efficiency and reliability. For energy saving and longer service life, a parallel pump system was supplied, with the valves and rotational speed controls approximating the system’s operating conditions closer to the designed conditions for different consumer loads. The developed optimization model employs a genetic algorithm (GA) aiming at the pumps’ maximum efficiency. A theoretical solution based on the Lagrange multiples method was proved. Experiments on two identical pumps were carried out. The presented model gives optimal input data for the pumps’ rotational speed and valve positions. The results show that control valves are especially helpful for improvement of a single pump’s efficiency and reliability. However, in the system of parallel pumps, throttling losses in the valves caused a significant decline in system efficiency. Therefore, the developed optimization model provides balance between efficiency and reliability by offering the suitable operating mode.
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Acknowledgments
This study was conducted as part of the National Science and Technology support Program (No. 2013BAF 01B02) and the China Postdoctoral Science Foundation (No. 2014M561758). This research was also supported by KSB AG. The authors wish to thank Prof. Hellmann and Dr. Paulus for their help.
References
Angel, P., Hernandez, J., and Agudelo, R. (1999). “Fuzzy expert system model for the operation of an urban water supply system.” Comput. Control Water Ind., 1, 449–457.
Bernier, M., and Bourret, B. (1999). “Pumping energy and variable frequency drives.” ASHRAE J., 41(12), 37–40.
Bunn, S. M., and Reynolds, L. (2009). “The energy-efficiency benefits of pump-scheduling optimization for potable water supplies.” IBM J. Res. Dev., 53(3), 5–13.
Burnell, D., Race, J., and Evans, P. (1993). “An overview of the trunk scheduling system for the london ring main.” Water Sci. Technol., 28(11–12), 99–109.
Chang, C., and van Zyl, J. (2014). “Optimal reliability-based design of bulk water supply systems.” J. Water Resour. Plann. Manage., 32–39.
European Commission. (2001). Study on improving the energy efficiency of pumps, Brussel, Belgium.
Giacomello, C., Kapelan, Z., and Nicolini, M. (2013). “Fast hybrid optimization method for effective pump scheduling.” J. Water Resour. Plann. Manage., 175–183.
León, C., Martín, S., Elena, J., and Luque, J. (2000). “EXPLORE-Hybrid expert system for water networks management.” J. Water Resour. Plann. Manage., 65–74.
Savic, D., and Walters, G. (1997). ”Genetic algorithms for least-cost design of water distribution networks.” J. Water Resour. Plann. Manage., 67–77.
Ulanicki, B., Kahler, J., and Coulbeck, B. (2008). “Modeling the efficiency and power characteristics of a pump group.” J. Water Resour. Plann. Manage., 88–93.
Van Zyl, J., Savic, D., and Walters, G. (2004). “Operational optimization of water distribution systems using a hybrid genetic algorithm.” J. Water Resour. Plann. Manage., 160–170.
Walski, T. (2010). “Your pumps may be stealing from you.” Proc. Water Environ. Fed., 2010(6), 552–559.
Wang, J., Chang, T, and Chen, J. (2009). “An enhanced genetic algorithm for bi-objective pump scheduling in water supply.” Expert Syst. Appl., 36(7), 10249–10258.
Yu, G., Powell, R., and Sterling, M. (1994). “Optimized pump scheduling in water distribution systems.” J. Optim. Theor. Appl., 83(3), 463–488.
Zhao, T., Cai, X., Lei, X., and Wang, H. (2012). “Improved dynamic programming for reservoir operation optimization with a concave objective function.” J. Water Resour. Plann. Manage., 590–596.
Zheng, F., Zecchin, A., Simpson, A., and Lambert, M. (2014). “Noncrossover dither creeping mutation-based genetic algorithm for pipe network optimization.” J. Water Resour. Plann. Manage., 553–557.
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Published In
Journal of Water Resources Planning and Management
Volume 141 • Issue 8 • August 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 9, 2013
Accepted: Oct 3, 2014
Published online: Nov 5, 2014
Discussion open until: Apr 5, 2015
Published in print: Aug 1, 2015
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