Optimization of Water Distribution System Operation with Multiple Tanks and Pumps: Application for Asmara, Eritrea’s Water Supply System
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 12, Issue 4
Abstract
Pump energy consumption is the major operational cost of water distribution systems (WDS). Defining better pump operation policies can reduce the cost and the enhance service provided. The nonlinear hydraulic behavior of WDS makes the pump scheduling problem complex, especially in large WDS in which several tanks and pumping stations coexist. The nonlinear hydraulic behavior of WDS is captured using numerical simulation. However, the required energy calculations were computed from the flow rate and head increase after the pumping stations. There are many optimization algorithms for the reduction of the operational cost. The majority of these algorithms are applied to hypothetical case studies with reduced feasible solution spaces. When the search space becomes large and many alternatives (pumps combinations) are possible, most of the proposed algorithms fail. Thus, the application of these algorithms in real case studies is very limited, and water utility managers still rely on human expertise to schedule pump operation rather than on automatic scheduling. This paper proposes the use of a genetic algorithm for large WDS with a many tanks and pumps. Offline hydraulic simulations allow the definition of water level bounds in the tanks. The combination of the pumps is determined a priori and the optimization algorithm is run in a reduced search space. These preparations allow the genetic algorithm to identify quickly the optimal policy and reduce the cost of energy consumption. The proposed framework was applied to the WDS of the city of Asmara, Eritrea, with 12 tanks and 9 pumping stations. The results showed that the tanks controlled by pumps worked perfectly and the water level was kept within the defined ranges. However, the constraints were not fulfilled for uncontrolled tanks. This application showed the limitations of methodologies proposed in the literature, and it is suggested that further techniques be found to schedule the pump working policies of large WDS while reducing the violations of constraints.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The fourth author acknowledges the financial support through Natural Sciences an Engineering Research Council of Canada under the Discovery Grant programs (RGPIN-2019-05584).
References
Bagirov, A. M., A. Barton, H. Mala-Jetmarova, A. Al Nuaimat, S. Ahmed, N. Sultanova, and J. Yearwood. 2013. “An algorithm for minimization of pumping costs in water distribution systems using a novel approach to pump scheduling.” Math. Comput. Modell. 57 (3–4): 873–886. https://doi.org/10.1016/j.mcm.2012.09.015.
Bagloee, S. A., M. Asadi, and M. Patriksson. 2018. “Minimization of water pumps’ electricity usage: A hybrid approach of regression models with optimization.” Expert Syst. Appl. 107 (Oct): 222–242. https://doi.org/10.1016/j.eswa.2018.04.027.
Balekelayi, N. 2010. “Operational optimization of water distribution networks using hybrid algorithms.” M.S. thesis, Water Science and Engineering, UNESCO-IHE Institute for Water Education.
Balekelayi, N., and S. Tesfamariam. 2017. “Optimization techniques used in design and operations of water distribution networks: A review and comparative study.” Sustainable Resilient Infrastruct. 2 (4): 153–168. https://doi.org/10.1080/23789689.2017.1328921.
Bohórquez, J., J. Saldarriaga, and D. Vallejo. 2015. “Pumping pattern optimization in order to reduce WDS operation costs.” Procedia Eng. 100 (119): 1069–1077. https://doi.org/10.1016/j.proeng.2015.08.936.
Bonvin, G., S. Demassey, C. Le Pape, N. Maïzi, V. Mazauric, and A. Samperio. 2017. “A convex mathematical program for pump scheduling in a class of branched water networks.” Appl. Energy 185 (Jan): 1702–1711. https://doi.org/10.1016/j.apenergy.2015.12.090.
Burnell, D., J. Race, and P. Evans. 1993. “An overview of the trunk scheduling system for the London ring main.” Water Sci. Technol. 28 (11–12): 99–109. https://doi.org/10.2166/wst.1993.0650.
CIA (Central Intelligence Agency). 2020. “The World Factbook.” Accessed September 13, 2020. https://www.cia.gov/the-world-factbook/countries/eritrea/.
Denison, E., M. Teklemariam, and D. Abraha. 2017. “Asmara: Africa’s modernist city (UNESCO World Heritage Nomination).” J. Archit. 22 (1): 11–53. https://doi.org/10.1080/13602365.2016.1276093.
Giacomello, C., Z. Kapelan, and M. Nicolini. 2013. “Fast hybrid optimization method for effective pump scheduling.” J. Water Resour. Plann. Manage. 139 (2): 175–183. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000239.
Goldberg, D. E. 1989. “Genetic algorithms in search.” In Optimization and machine learning. Reading, MA: Addison-Wesley.
Ibarra, D., and J. Arnal. 2014. “Parallel programming techniques applied to water pump scheduling problems.” J. Water Resour. Plann. Manage. 140 (7): 06014002. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000439.
JICA (Japan International Cooperation Agency). 2016. “Project completion report on Asmara water supply infrastructure (data collection and management) project in the state of Eritrea.” Accessed September 14, 2020. https://openjicareport.jica.go.jp/pdf/12269338.pdf.
Jiménez, Y. M., J. C. Palacio, and A. Nowé. 2020. “Multi-agent reinforcement learning tool for job shop scheduling problems.” In Proc., Int. Conf. on Optimization and Learning, 3–12. Cham, Switzerland: Springer.
Lansey, K. E., and K. Awumah. 1994. “Optimal pump operations considering pump switches.” J. Water Resour. Plann. Manage. 120 (1): 17–35. https://doi.org/10.1061/(ASCE)0733-9496(1994)120:1(17).
Mackle, G., G. Savic, and G. A. Walters. 1995. “Application of genetic algorithms to pump scheduling for water supply.” In Proc., 1st Int. Conf. on Genetic Algorithms in Engineering Systems: Innovations and Applications, 400–405. Edinburgh, UK: Institution of Engineering and Technology.
Mahar, P., and R. Singh. 2014. “Optimal design of pumping mains considering pump characteristics.” J. Pipeline Syst. Eng. Pract. 5 (1): 04013010. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000157.
Marchi, A., and A. R. Simpson. 2013. “Correction of the EPANET inaccuracy in computing the efficiency of variable speed pumps.” J. Water Resour. Plann. Manage. 139 (4): 456–459. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000273.
Nitivattananon, V., E. C. Sadowski, and R. G. Quimpo. 1996. “Optimization of water supply system operation.” J. Water Resour. Plann. Manage. 122 (5): 374–384. https://doi.org/10.1061/(ASCE)0733-9496(1996)122:5(374).
Pandey, S., R. P. Singh, and P. S. Mahar. 2020. “Optimal pipe sizing and operation of multistage centrifugal pumps for water supply.” J. Pipeline Syst. Eng. Pract. 11 (2): 04020007. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000447.
Pasha, M. F. K., and K. Lansey. 2009. “Optimal pump scheduling by linear programming.” In Proc., World Environmental and Water Resources Congress 2009: Great Rivers, 1–10. Reston, VA: ASCE.
Puleo, V., M. Morley, G. Freni, and D. Savić. 2014. “Multi-stage linear programming optimization for pump scheduling.” Procedia Eng. 70: 1378–1385. https://doi.org/10.1016/j.proeng.2014.02.152.
Rodwell, D. 2018. “The historic urban landscape and the geography of urban heritage.” Historic Environ. Policy Pract. 9 (3–4): 180–206. https://doi.org/10.1080/17567505.2018.1517140.
Rossman, L. A. 2000. EPANET 2: Users manual. Cincinnati: USEPA, Office of Research and Development.
Savic, D. A., and G. A. Walters. 1997. “Genetic algorithms for least-cost design of water distribution networks.” J. Water Resour. Plann. Manage. 123 (2): 67–77. https://doi.org/10.1061/(ASCE)0733-9496(1997)123:2(67).
Statistics Canada. 2019. “Operation and maintenance costs of drinking water plants by production volume and main source water type.” Accessed November 26, 2019. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3810026901.
Tolson, B. A., M. Asadzadeh, H. R. Maier, and A. Zecchin. 2009. “Hybrid discrete dynamically dimensioned search (HD-DDS) algorithm for water distribution system design optimization.” Water Resour. Res. 45 (12): W12416. https://doi.org/10.1029/2008WR007673.
Ulanicki, B., J. Kahler, and H. See. 2007. “Dynamic optimization approach for solving an optimal scheduling problem in water distribution systems.” J. Water Resour. Plann. Manage. 133 (1): 23–32. https://doi.org/10.1061/(ASCE)0733-9496(2007)133:1(23).
van Zyl, J. E., D. A. Savic, and G. A. Walters. 2004. “Operational optimization of water distribution systems using a hybrid genetic algorithm.” J. Water Resour. Plann. Manage. 130 (2): 160–170. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:2(160).
Wu, P., Z. Lai, D. Wu, and L. Wang. 2015. “Optimization research of parallel pump system for improving energy efficiency.” J. Water Resour. Plann. Manage. 141 (8): 04014094. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000493.
Zeraebruk, K. N. 2017. “Development of decision support tools for sustainable water development and management in urban areas: Case study of Asmara water supply system, Eritrea.” Ph.D. thesis, College of Engineering and Technology, Jomo Kenyatta Univ. of Agriculture and Technology.
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Journal of Pipeline Systems Engineering and Practice
Volume 12 • Issue 4 • November 2021
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© 2021 American Society of Civil Engineers.
History
Received: Feb 26, 2020
Accepted: Apr 20, 2021
Published online: Jun 30, 2021
Published in print: Nov 1, 2021
Discussion open until: Nov 30, 2021
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