Abstract
Flow assurance, aimed to ensure the availability of water flow rate and the sufficiency of pressure on each customer, is one of objectives that should be achieved by water supplying companies. An essential step before dealing with it is to predict pressure distribution on each node. Using the analogy of Kirchoff’s Law for the electrical current to the flow of water in pipelines, a non-linear equation system involving fluid dynamics modeling is constructed and used for determining pressure distribution. It is obvious that the system is not a simple one since it contains many non-linear equations expressing the complexity of the network. In this study, we implement Particle Swarm Optimization (PSO) to solve the system by transforming a root-finding task into an optimization problem. Finally, we present a case study using Hanoi network along with a result compared with EPANET, Firefly Algorithm (FA), and a combination of Genetic Algorithm (GA) and Newton’s method.
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References
Prasad, T.D., Park, N.S.: Multiobjective genetic algorithms for design of water distribution networks. J. Water Res. Planning Manage. (2003)
Iglesias, P.L., Mora, D., Martínez, F.J., Fuertes, V.S.: Study of sensitivity of the parameters of a genetic algorithm for design of water distribution networks. J. Urban Environ. Eng. (JUEE) 1(2) (2008)
Liong, S.Y., Atiquzzaman, M.: Optimal design of water distribution network using shuffled complex evolution. J. Inst. Eng. 44(1), 93–107 (2004). Singapore
da Conceicao Cunha, M., Ribeiro, L.: Tabu search algorithms for water network optimization. Eur. J. Oper. Res. 157(3), 746–758 (2004)
Riza, L.S., Kusnendar, J., Munir, H., R.N., Sidarto, K.A.: Determining the pressure distribution on water pipeline networks using the firefly algorithm. In: 2016 7th International Conference on Intelligent Systems, Modelling, and Simulation. IEEE (2016, to appear)
Savic, D.A., Walters, G.A.: Genetic algorithms for least-cost design of water distribution networks. J. Water Resour. Plan. Manage. 123(2), 67–77 (1997)
Simpson, A., Elhay, S.: Jacobian matrix for solving water distribution system equations with the Darcy-Weisbach head-loss model. J. Hydraul. Eng. 137(6), 696–700 (2010)
Walski, T.M., Chase, D.V., Savic, D.A., Grayman, W.M., Beckwith, S., Koelle, E., et al.: Advanced Water Distribution Modeling and Management. Haestad press, Waterbury (2003)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Proceedings, vol. 4, pp. 1942–1948 (1995)
Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, NewYork (2010)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 81–86. IEEE (2001)
Dhillon, S., Lather, J., Marwaha, S.: Multi objective load frequency control using hybrid bacterial foraging and particle swarm optimized pi controller. Int. J. Electr. Power Energy Syst. 79, 196–209 (2016)
Zhou, Y., Yan, Y., Huang, X., Kong, L.: Optimal scheduling of multiple geosynchronous satellites refueling based on a hybrid particle swarm optimizer. Aerosp. Sci. Technol. 47, 125–134 (2015)
Zhao, X., Xu, W., Ma, Y., Hu, F.: Scenario-based multi-objective optimum allocation model for earthquake emergency shelters using a modified particle swarm optimization algorithm: a case study in chaoyang district. PloS one 10(12), e0144455 (2015). Beijing, China
Bernoulli, D.: Hydrodynamica, sive De viribus et motibus fluidorum commentarii. Opus academicum ab auctore, dum Petropoli ageret, congestum. Sumptibus Johannis Reinholdi Dulseckeri (1738)
Fujiwara, O., Khang, D.B.: A two-phase decomposition method for optimal design of looped water distribution networks. Water Resour. Res. 26(4), 539–549 (1990)
Rossman, L.: The EPANET water quality model. Technical report, Environmental Protection Agency, Cincinnati, OH (United States) (1995)
Sidarto, K.A., Siregar, S., Amoranto, T., Riza, L.S., Darmadi, M., Dewi, S.: Predicting pressure distribution in a complex water pipeline network system using combination of genetic algorithm and newtons method. In: SEAMS - GMU International Conference on Mathematics and its Applications (2007)
Riza, L.S., Bergmeir, C., Herrera, F., Benítez, J.M.: FRBS: fuzzy rule-based systems for classification and regression in R. J. Stat. Softw. 65(1), 1–30 (2015)
Riza, L.S., Janusz, A., Bergmeir, C., Cornelis, C., Herrera, F., Ślȩzak, D., Benítez, J.M.: Implementing algorithms of rough set theory and fuzzy rough set theory in the R package RoughSets. Inf. Sci. 287, 68–89 (2014)
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Riza, L.S., Azmi, A.F., Waslaluddin, Rahman, E.F., Sidarto, K.A. (2016). Particle Swarm Optimization for Calculating Pressure on Water Distribution Systems. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_38
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DOI: https://doi.org/10.1007/978-3-319-41000-5_38
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