Quasi-static Flow Model for Predicting the Extreme Values of Air Pocket Pressure in Draining and Filling Operations in Single Water Installations

[EN] Inertial models have been used by researchers to simulate the draining and filling processes in water pipelines, based on the evolution of the main hydraulic and thermodynamic variables. These models use complex differential equations, which are solved using advanced numerical codes. In this study, a quasi-static flow model is developed to study these operations in hydraulic installations. The quasi-static flow model represents a simplified formulation compared with inertial flow models, in which its numerical resolution is easier because only algebraic equations must be addressed. Experimental measurements of air pocket pressure patterns were conducted in a 4.36 m long single pipeline with an internal diameter of 42 mm. Comparisons between measured and computed air pocket pressure oscillations indicate how the quasi-static flow model can predict extreme values of air pocket pressure for experimental runs, demonstrating the possibility of selecting stiffness and pipe classes in actual pipelines using this model. Two case studies were analysed to determine the behaviour of the quasi-static flow model in large water pipelines.This research and the APC were funded by the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), grant number 1180660.Coronado-Hernández, ÓE.; Fuertes-Miquel, VS.; Mora-Meliá, D.; Salgueiro, Y. (2020). Quasi-static Flow Model for Predicting the Extreme Values of Air Pocket Pressure in Draining and Filling Operations in Single Water Installations. Water. 12(3):1-16. https://doi.org/10.3390/w12030664S116123Abreu, J., Cabrera, E., Izquierdo, J., & García-Serra, J. (1999). Flow Modeling in Pressurized Systems Revisited. Journal of Hydraulic Engineering, 125(11), 1154-1169. doi:10.1061/(asce)0733-9429(1999)125:11(1154)Izquierdo, J., Fuertes, V. S., Cabrera, E., Iglesias, P. L., & Garcia-Serra, J. (1999). Pipeline start-up with entrapped air. Journal of Hydraulic Research, 37(5), 579-590. doi:10.1080/00221689909498518Simpson, A. R., & Wylie, E. B. (1991). Large Water‐Hammer Pressures for Column Separation in Pipelines. Journal of Hydraulic Engineering, 117(10), 1310-1316. doi:10.1061/(asce)0733-9429(1991)117:10(1310)Zhou, L., Liu, D., Karney, B., & Wang, P. (2013). Phenomenon of White Mist in Pipelines Rapidly Filling with Water with Entrapped Air Pockets. Journal of Hydraulic Engineering, 139(10), 1041-1051. doi:10.1061/(asce)hy.1943-7900.0000765Zhou, L., & Liu, D. (2013). Experimental investigation of entrapped air pocket in a partially full water pipe. Journal of Hydraulic Research, 51(4), 469-474. doi:10.1080/00221686.2013.785985Coronado-Hernández, O., Fuertes-Miquel, V., Besharat, M., & Ramos, H. (2017). Experimental and Numerical Analysis of a Water Emptying Pipeline Using Different Air Valves. Water, 9(2), 98. doi:10.3390/w9020098Coronado-Hernández, Ó. E., Besharat, M., Fuertes-Miquel, V. S., & Ramos, H. M. (2019). Effect of a Commercial Air Valve on the Rapid Filling of a Single Pipeline: a Numerical and Experimental Analysis. Water, 11(9), 1814. doi:10.3390/w11091814Vasconcelos, J. G., & Wright, S. J. (2008). Rapid Flow Startup in Filled Horizontal Pipelines. Journal of Hydraulic Engineering, 134(7), 984-992. doi:10.1061/(asce)0733-9429(2008)134:7(984)Fuertes-Miquel, V. S., Coronado-Hernández, O. E., Iglesias-Rey, P. L., & Mora-Meliá, D. (2018). Transient phenomena during the emptying process of a single pipe with water–air interaction. Journal of Hydraulic Research, 57(3), 318-326. doi:10.1080/00221686.2018.1492465Fuertes-Miquel, V. S., Coronado-Hernández, O. E., Mora-Meliá, D., & Iglesias-Rey, P. L. (2019). Hydraulic modeling during filling and emptying processes in pressurized pipelines: a literature review. Urban Water Journal, 16(4), 299-311. doi:10.1080/1573062x.2019.1669188Besharat, M., Coronado-Hernández, O. E., Fuertes-Miquel, V. S., Viseu, M. T., & Ramos, H. M. (2018). Backflow air and pressure analysis in emptying a pipeline containing an entrapped air pocket. Urban Water Journal, 15(8), 769-779. doi:10.1080/1573062x.2018.1540711Besharat, M., Coronado-Hernández, O. E., Fuertes-Miquel, V. S., Viseu, M. T., & Ramos, H. M. (2019). Computational fluid dynamics for sub-atmospheric pressure analysis in pipe drainage. Journal of Hydraulic Research, 58(4), 553-565. doi:10.1080/00221686.2019.1625819Laanearu, J., Annus, I., Koppel, T., Bergant, A., Vučković, S., Hou, Q., … van’t Westende, J. M. C. (2012). Emptying of Large-Scale Pipeline by Pressurized Air. Journal of Hydraulic Engineering, 138(12), 1090-1100. doi:10.1061/(asce)hy.1943-7900.0000631Tijsseling, A. S., Hou, Q., Bozkuş, Z., & Laanearu, J. (2015). Improved One-Dimensional Models for Rapid Emptying and Filling of Pipelines. Journal of Pressure Vessel Technology, 138(3). doi:10.1115/1.4031508Malekpour, A., Karney, B. W., & Nault, J. (2016). Physical Understanding of Sudden Pressurization of Pipe Systems with Entrapped Air: Energy Auditing Approach. Journal of Hydraulic Engineering, 142(2), 04015044. doi:10.1061/(asce)hy.1943-7900.0001067Noto, L., & Tucciarelli, T. (2001). DORA Algorithm for Network Flow Models with Improved Stability and Convergence Properties. Journal of Hydraulic Engineering, 127(5), 380-391. doi:10.1061/(asce)0733-9429(2001)127:5(380)Zhou, L., Liu, D., & Ou, C. (2011). Simulation of Flow Transients in a Water Filling Pipe Containing Entrapped Air Pocket with VOF Model. Engineering Applications of Computational Fluid Mechanics, 5(1), 127-140. doi:10.1080/19942060.2011.11015357SaemI, S., Raisee, M., Cervantes, M. J., & Nourbakhsh, A. (2018). Computation of two- and three-dimensional water hammer flows. Journal of Hydraulic Research, 57(3), 386-404. doi:10.1080/00221686.2018.1459892Apollonio, C., Balacco, G., Fontana, N., Giugni, M., Marini, G., & Piccinni, A. (2016). Hydraulic Transients Caused by Air Expulsion During Rapid Filling of Undulating Pipelines. Water, 8(1), 25. doi:10.3390/w8010025Wang, L., Wang, F., Karney, B., & Malekpour, A. (2017). Numerical investigation of rapid filling in bypass pipelines. Journal of Hydraulic Research, 55(5), 647-656. doi:10.1080/00221686.2017.1300193Coronado-Hernández, O. E., Fuertes-Miquel, V. S., Besharat, M., & Ramos, H. M. (2018). Subatmospheric pressure in a water draining pipeline with an air pocket. Urban Water Journal, 15(4), 346-352. doi:10.1080/1573062x.2018.1475578Ramezani, L., Karney, B., & Malekpour, A. (2016). Encouraging Effective Air Management in Water Pipelines: A Critical Review. Journal of Water Resources Planning and Management, 142(12), 04016055. doi:10.1061/(asce)wr.1943-5452.0000695Martins, S. C., Ramos, H. M., & Almeida, A. B. (2015). Conceptual analogy for modelling entrapped air action in hydraulic systems. Journal of Hydraulic Research, 53(5), 678-686. doi:10.1080/00221686.2015.1077353Zhou, F., Hicks, F. E., & Steffler, P. M. (2002). Transient Flow in a Rapidly Filling Horizontal Pipe Containing Trapped Air. Journal of Hydraulic Engineering, 128(6), 625-634. doi:10.1061/(asce)0733-9429(2002)128:6(625)Cabrera, E., Abreu, J., Pérez, R., & Vela, A. (1992). Influence of Liquid Length Variation in Hydraulic Transients. Journal of Hydraulic Engineering, 118(12), 1639-1650. doi:10.1061/(asce)0733-9429(1992)118:12(1639

Search Space Reduction for Genetic Algorithms Applied to Drainage Network Optimization Problems

[EN] In recent years, a significant increase in the number of extreme rains around the world has been observed, which has caused an overpressure of urban drainage networks. The lack of capacity to evacuate this excess water generates the need to rehabilitate drainage systems. There are different rehabilitation methodologies that have proven their validity; one of the most used is the heuristic approach. Within this approach, the use of genetic algorithms has stood out for its robustness and effectiveness. However, the problem to be overcome by this approach is the large space of solutions that algorithms must explore, affecting their efficiency. This work presents a method of search space reduction applied to the rehabilitation of drainage networks. The method is based on reducing the initially large search space to a smaller one that contains the optimal solution. Through iterative processes, the search space is gradually reduced to define the final region. The rehabilitation methodology contemplates the optimization of networks using the joint work of the installation of storm tanks, replacement of pipes, and implementation of hydraulic control elements. The optimization model presented uses a pseudo genetic algorithm connected to the SWMM model through a toolkit. Optimization problems consider a large number of decision variables, and could require a huge computational effort. For this reason, this work focuses on identifying the most promising region of the search space to contain the optimal solution and to improve the efficiency of the process. Finally, this method is applied in real networks to show its validity.This work was supported by the Program Fondecyt Regular (Project No. 1210410 and Project No. 1180660) of the Agencia Nacional de Investigación y Desarrollo (ANID), Chile.Bayas-Jiménez, L.; Martínez-Solano, FJ.; Iglesias Rey, PL.; Mora-Meliá, D. (2021). Search Space Reduction for Genetic Algorithms Applied to Drainage Network Optimization Problems. Water. 13(15):1-24. https://doi.org/10.3390/w13152008S124131

Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation

[EN] Drainage networks are civil constructions which do not generally attract the attention of decision-makers. However, they are of crucial importance for cities; this can be seen when a city faces floods resulting in extensive and expensive damage. The increase of rain intensity due to climate change may cause deficiencies in drainage networks built for certain defined flows which are incapable of coping with sudden increases, leading to floods. This problem can be solved using different strategies; one is the adaptation of the network through rehabilitation. A way to adapt the traditional network approach consists of substituting some pipes for others with greater diameters. More recently, the installation of storm tanks makes it possible to temporarily store excess water. Either of these solutions can be expensive, and an economic analysis must be done. Recent studies have related flooding with damage costs. In this work, a novel solution combining both approaches (pipes and tanks) is studied. A multi-objective optimization algorithm based on the NSGA-II is proposed for the rehabilitation of urban drainage networks through the substitution of pipes and the installation of storage tanks. Installation costs will be o set by damage costs associated with flooding. As a result, a set of optimal solutions that can be implemented based on the objectives to be achieved by municipalities or decisions makers. The methodology is finally applied to a real network located in the city of Bogotá, Colombia.This work was supported by the Program Fondecyt Regular (Project 1180660) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile.Ngamalieu-Nengoue, UA.; Martínez-Solano, FJ.; Iglesias Rey, PL.; Mora-Meliá, D. (2019). Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation. Water. 11(5). https://doi.org/10.3390/w11050935S115Kordana, S. (2018). The identification of key factors determining the sustainability of stormwater systems. E3S Web of Conferences, 45, 00033. doi:10.1051/e3sconf/20184500033Yazdi, J., Lee, E. H., & Kim, J. H. (2015). Stochastic Multiobjective Optimization Model for Urban Drainage Network Rehabilitation. Journal of Water Resources Planning and Management, 141(8), 04014091. doi:10.1061/(asce)wr.1943-5452.0000491Starzec, M., Dziopak, J., Słyś, D., Pochwat, K., & Kordana, S. (2018). Dimensioning of Required Volumes of Interconnected Detention Tanks Taking into Account the Direction and Speed of Rain Movement. Water, 10(12), 1826. doi:10.3390/w10121826Mailhot, A., & Duchesne, S. (2010). Design Criteria of Urban Drainage Infrastructures under Climate Change. Journal of Water Resources Planning and Management, 136(2), 201-208. doi:10.1061/(asce)wr.1943-5452.0000023Gulizia, C., & Camilloni, I. (2014). Comparative analysis of the ability of a set of CMIP3 and CMIP5 global climate models to represent precipitation in South America. International Journal of Climatology, 35(4), 583-595. doi:10.1002/joc.4005Ma, M., He, B., Wan, J., Jia, P., Guo, X., Gao, L., … Hong, Y. (2018). Characterizing the Flash Flooding Risks from 2011 to 2016 over China. Water, 10(6), 704. doi:10.3390/w10060704Kirshen, P., Caputo, L., Vogel, R. M., Mathisen, P., Rosner, A., & Renaud, T. (2015). Adapting Urban Infrastructure to Climate Change: A Drainage Case Study. Journal of Water Resources Planning and Management, 141(4), 04014064. doi:10.1061/(asce)wr.1943-5452.0000443Moselhi, O., & Shehab-Eldeen, T. (2000). Classification of Defects in Sewer Pipes Using Neural Networks. Journal of Infrastructure Systems, 6(3), 97-104. doi:10.1061/(asce)1076-0342(2000)6:3(97)Driessen, P., Hegger, D., Kundzewicz, Z., van Rijswick, H., Crabbé, A., Larrue, C., … Wiering, M. (2018). Governance Strategies for Improving Flood Resilience in the Face of Climate Change. Water, 10(11), 1595. doi:10.3390/w10111595Reyna, S. M., Vanegas, J. A., & Khan, A. H. (1994). Construction Technologies for Sewer Rehabilitation. Journal of Construction Engineering and Management, 120(3), 467-487. doi:10.1061/(asce)0733-9364(1994)120:3(467)Abraham, D. M., Wirahadikusumah, R., Short, T. J., & Shahbahrami, S. (1998). Optimization Modeling for Sewer Network Management. Journal of Construction Engineering and Management, 124(5), 402-410. doi:10.1061/(asce)0733-9364(1998)124:5(402)Sebti, A., Fuamba, M., & Bennis, S. (2016). Optimization Model for BMP Selection and Placement in a Combined Sewer. Journal of Water Resources Planning and Management, 142(3), 04015068. doi:10.1061/(asce)wr.1943-5452.0000620Zahmatkesh, Z., Burian, S. J., Karamouz, M., Tavakol-Davani, H., & Goharian, E. (2015). Low-Impact Development Practices to Mitigate Climate Change Effects on Urban Stormwater Runoff: Case Study of New York City. Journal of Irrigation and Drainage Engineering, 141(1), 04014043. doi:10.1061/(asce)ir.1943-4774.0000770Mora-Melià, D., López-Aburto, C., Ballesteros-Pérez, P., & Muñoz-Velasco, P. (2018). Viability of Green Roofs as a Flood Mitigation Element in the Central Region of Chile. Sustainability, 10(4), 1130. doi:10.3390/su10041130Ugarelli, R., & Di Federico, V. (2010). Optimal Scheduling of Replacement and Rehabilitation in Wastewater Pipeline Networks. Journal of Water Resources Planning and Management, 136(3), 348-356. doi:10.1061/(asce)wr.1943-5452.0000038Ngamalieu-Nengoue, U., Iglesias-Rey, P., Martínez-Solano, F., Mora-Meliá, D., & Saldarriaga Valderrama, J. (2019). Urban Drainage Network Rehabilitation Considering Storm Tank Installation and Pipe Substitution. Water, 11(3), 515. doi:10.3390/w11030515Lee, E., & Kim, J. (2017). Development of Resilience Index Based on Flooding Damage in Urban Areas. Water, 9(6), 428. doi:10.3390/w9060428Iglesias-Rey, P. L., Martínez-Solano, F. J., Saldarriaga, J. G., & Navarro-Planas, V. R. (2017). Pseudo-genetic Model Optimization for Rehabilitation of Urban Storm-water Drainage Networks. Procedia Engineering, 186, 617-625. doi:10.1016/j.proeng.2017.03.278Fadel, A. W., Marques, G. F., Goldenfum, J. A., Medellín-Azuara, J., & Tilmant, A. (2018). Full Flood Cost: Insights from a Risk Analysis Perspective. Journal of Environmental Engineering, 144(9), 04018071. doi:10.1061/(asce)ee.1943-7870.0001414Duan, H.-F., Li, F., & Yan, H. (2016). Multi-Objective Optimal Design of Detention Tanks in the Urban Stormwater Drainage System: LID Implementation and Analysis. Water Resources Management, 30(13), 4635-4648. doi:10.1007/s11269-016-1444-1Starzec, M. (2018). A critical evaluation of the methods for the determination of required volumes for detention tank. E3S Web of Conferences, 45, 00088. doi:10.1051/e3sconf/20184500088Pochwat, K. B., & Słyś, D. (2018). Application of Artificial Neural Networks in the Dimensioning of Retention Reservoirs. Ecological Chemistry and Engineering S, 25(4), 605-617. doi:10.1515/eces-2018-0040Cunha, M. C., Zeferino, J. A., Simões, N. E., & Saldarriaga, J. G. (2016). Optimal location and sizing of storage units in a drainage system. Environmental Modelling & Software, 83, 155-166. doi:10.1016/j.envsoft.2016.05.015Martino, G. D., De Paola, F., Fontana, N., Marini, G., & Ranucci, A. (2011). Pollution Reduction in Receivers: Storm-Water Tanks. Journal of Urban Planning and Development, 137(1), 29-38. doi:10.1061/(asce)up.1943-5444.0000037Andrés-Doménech, I., Montanari, A., & Marco, J. B. (2012). Efficiency of Storm Detention Tanks for Urban Drainage Systems under Climate Variability. Journal of Water Resources Planning and Management, 138(1), 36-46. doi:10.1061/(asce)wr.1943-5452.0000144Wang, M., Sun, Y., & Sweetapple, C. (2017). Optimization of storage tank locations in an urban stormwater drainage system using a two-stage approach. Journal of Environmental Management, 204, 31-38. doi:10.1016/j.jenvman.2017.08.024Cunha, M. C., Zeferino, J. A., Simões, N. E., Santos, G. L., & Saldarriaga, J. G. (2017). A decision support model for the optimal siting and sizing of storage units in stormwater drainage systems. International Journal of Sustainable Development and Planning, 12(01), 122-132. doi:10.2495/sdp-v12-n1-122-132Dziopak, J. (2018). A wastewater retention canal as a sewage network and accumulation reservoir. E3S Web of Conferences, 45, 00016. doi:10.1051/e3sconf/20184500016Słyś, D. (2018). An innovative retention canal – a case study. E3S Web of Conferences, 45, 00084. doi:10.1051/e3sconf/20184500084Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Martínez-Solano, F., Iglesias-Rey, P., Saldarriaga, J., & Vallejo, D. (2016). Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization. Water, 8(6), 259. doi:10.3390/w8060259Wang, Q., Zhou, Q., Lei, X., & Savić, D. A. (2018). Comparison of Multiobjective Optimization Methods Applied to Urban Drainage Adaptation Problems. Journal of Water Resources Planning and Management, 144(11), 04018070. doi:10.1061/(asce)wr.1943-5452.0000996Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., & Ballesteros-Pérez, P. (2015). Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing. Water Resources Management, 29(13), 4817-4831. doi:10.1007/s11269-015-1092-xMora-Melià, D., Martínez-Solano, F. J., Iglesias-Rey, P. L., & Gutiérrez-Bahamondes, J. H. (2017). Population Size Influence on the Efficiency of Evolutionary Algorithms to Design Water Networks. Procedia Engineering, 186, 341-348. doi:10.1016/j.proeng.2017.03.20

Design of water distribution networks using a pseudo-genetic algorithm and sensitivity of genetic operators

[EN] Genetic algorithms (GA) are optimization techniques that are widely used in the design of water distribution networks. One of the main disadvantages of GA is positional bias, which degrades the quality of the solution. In this study, a modified pseudo-genetic algorithm (PGA) is presented. In a PGA, the coding of chromosomes is performed using integer coding; in a traditional GA, binary coding is utilized. Each decision variable is represented by only one gene. This variation entails a series of special characteristics in the definition of mutation and crossover operations. Some benchmark networks have been used to test the suitability of a PGA for designing water distribution networks. More than 50,000 simulations were conducted with different sets of parameters. A statistical analysis of the obtained solutions was also performed. Through this analysis, more suitable values of mutation and crossover probabilities were discovered for each case. The results demonstrate the validity of the method. Optimum solutions are not guaranteed in any heuristic method. Hence, the concept of a good solution is introduced. A good solution is a design solution that does not substantially exceed the optimal solution that is obtained from the simulations. This concept may be useful when the computational cost is critical. The main conclusion derived from this study is that a proper combination of population and crossover and mutation probabilities leads to a high probability that good solutions will be obtained[This work was supported by the project DPI2009-13674 (OPERAGUA) of the Direccion General de Investigacion y Gestion del Plan Nacional de I + D + I del Ministerio de Ciencia e Innovacion, Spain.Mora Meliá, D.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Fuertes Miquel, VS. (2013). Design of water distribution networks using a pseudo-genetic algorithm and sensitivity of genetic operators. Water Resources Management. 27(12):4149-4162. https://doi.org/10.1007/s11269-013-0400-6S414941622712Alperovits E, Shamir U (1977) Design of optimal water distribution systems. Water Resour Res 13(6):885–900Balla M, Lingireddy S (2000) Distributed genetic algorithm model on network of personal computers. J Comput Civ Eng 14(3):199–205. doi: 10.1061/(ASCE)0887-3801(2000)14:3(199)Baños R, Gil C, Agulleiro JI, Reca J (2007) A memetic algorithm for water distribution network design. Advances in Soft Computing 39:279–289. doi: 10.1007/978-3-540-70706-6_26Cisty M (2010) Hybrid genetic algorithm and linear programming method for least-cost design of water distribution systems. Water Resour Manage 24(1):1–24. doi: 10.1007/s1269-009-9434-1Chung G, Lansey K (2008) Application of the shuffled frog leaping algorithm for the optimization of a general large-scale in a watersupply system. Water Resour Manage 23:797–823. doi: 10.1007/s11269-008-9300-6Cunha MC, Sousa J (1999) Water distribution network design optimization: simulated annealing approach. J Water Resour Plann Manage 125(4):215–221. doi: 10.1061/(ASCE)0733-9496(1999)125:4(215)Eusuff M, Lansey K (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plann Manage 129(3):210–225. doi: 10.1061/(ASCE)0733-9496(2003)129:3(210)Fujiwara O, Khang DB (1990) A two phase decomposition method for optimal design of looped water distribution network. Water Resour Res 26(4):539–549. doi: 10.1029/WR026i004p00539Geem ZW (2006) Optimal cost design of water distribution networks using harmony search. Eng Optimiz 38(3):259–277. doi: 10.1080/03052150500467430Goldberg DE, Kuo CH (1987) Genetic algorithms in pipeline optimization. J Comput Civil Eng 1(2):128–141. doi: 10.1061/(ASCE)0887-3801(1987)1:2(128)Goulter IC, Morgan DR (1985) An integrated approach to the layout and design of water distribution systems. Civil Eng Syst 2(2):104–113. doi: 10.1080/02630258508970389Halhal D, Walters GA, Ouazar D, Savic DA (1997) Water network rehabilitation with structured messy genetic algorithms. J Water Resour Plann Manage 123(3):137–147. doi: 10.1061/(ASCE)0733-9496(1997)123:3(137)Iglesias-Rey PL, Martínez-Solano FJ, Mora-Meliá D, Ribelles-Aguilar JV (2012) The battle water networks II: Combination of meta-heuristic techniques with the concept of setpoint function in water network optimization algorithms. In: Proc. 14th Water Distribution Systems Analysis symposium (WDSA), Engineers Australia, Adelaide, AustraliaJin YX, Cheng HZ, Yan J, Zhang L (2007) New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electr Pow Syst Res 77(3–4):227–233. doi: 10.1016/j.epsr.2006.02.016Lansey KE, Mays LW (1989). Optimization model for design of water distribution systems. Reliability analysis of water distribution systems. In: L. R. Mays (ed) ASCE: Reston, VaLouati M, Benabdallah S, Lebdi F, Milutin D (2011) Application of a genetic algorithm for the optimization of a complex reservoir system in Tunisia. Water Resour Manage 25(10):2387–2404. doi: 10.1007/s11269-011-9814-1Matías A (2003) “Diseño de redes de distribución de agua contemplando la fiabilidad mediante Algoritmos Genéticos”. Ph.D. Thesis, Universidad Politécnica de Valencia, ValenciaNazif S, Karamouz M, Tabesh M, Moridi A (2010) Pressure management model for urban water distribution networks. Water Resour Manage 24(3):437–458. doi: 10.1007/s11269-009-9454-xPrasad DT, Park NS (2004) Multiobjective genetic algorithms for design of water distribution networks. J Water Resour Plann Manage 130(1):73–82. doi: 10.1061/(ASCE)0733-9496(2004)130:1(73)Reca J, Martinez J (2006) Genetic algorithms for the design of looped irrigation water distribution networks. Water Resour Res 42(5):W05416. doi: 10.1029/2005WR004383Reca J, Martinez J, Gil C, Baños R (2008) Application of several meta-heuristic techniques to the optimization of real looped water distribution networks. Water Resour Manage 22(10):1367–1379. doi: 10.1007/s11269-007-9230-8Rossman LA (2000) EPANET 2.0 User’s manual. EPA/600/R-00/057, 2000Savic DA, Walters GA (1997) Genetic algorithms for least-cost design of water distribution systems. J Water Resour Plann Manage 123(2):67–77. doi: 10.1061/(ASCE)0733-9496(1997)123:2(67)Su YL, Mays LW, Duan N, Lansey KE (1987) Reliability based optimization model for water distribution systems. J Hydraul Eng 113(12):1539–1556. doi: 10.1061/(ASCE)0733-9429(1987)113:12(1539)Tsai FTC, Katiyar V, Toy D, Goff RA (2008) Conjunctive management of large-scale pressurized water distribution and groundwater systems in semi-arid area with parallel genetic algorithm. Water Resour Manage 23(8):1497–1517. doi: 10.1007/s11269-008-9338-5Vairavamoorthy K, Ali M (2000) Optimal design of water distribution systems using genetic algorithms. Comput Aided Civ Infrastruc Eng 15(5):374–382. doi: 10.1111/0885-9507.00201Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resour Res 27(9):2467–2471. doi: 10.1029/91WR0130

Exact skeletonization method in water distribution systems for hydraulic and quality models

[EN] A mathematical model is a powerful tool for simulating different scenarios that occur in a water distribution network without making physical experimentation. According to the objectives, a model can be classified into three categories: layout, design and operation. Furthermore, the level of detail is strongly related to the objective that the model tries to achieve. However, bigger amount of information does not mean better accuracy. For example, a fully detailed mathematical model of the network would lead to know every single connection. Usually, this information is so difficult to compile as imprecise. Therefore, one of the most important stages in elaborating a model consists of the model simplification, also known as skeletonization. During the works made for model skeletonization some assumptions are made. Most of the times, these assumptions may produce significant errors. Among the different techniques for network skeletonization, series pipe removal is one of the most used. It consists of replacing several adjacent pipes with a single one which must present the same head losses than the pipes being substituted. When there are no intermediate consumptions the problem has been effectively solved. The problem arises when a demand appears in one of the pipes being removed. It has been demonstrated that methods which assume constant roughness coefficients (either Hazen-Williams or Darcy equations) produce errors in the head losses. These errors may be even higher if travel time is included as a restriction, for example in water quality models. This paper reviews the most common techniques for serial pipes association. The error will be evaluated in both hydraulic and quality models. Finally, a method for exact substitution of serial pipes with intermediate demands is proposed. This method imposes two restrictions (head losses and travel time) and gives exact results when the flow direction is known. The method is tested with an example that highlights the results.This work was supported by the projects “OPERAGUA”, (Project DPI2009-13674, Spain) and by the Program Initiation into research (Project 11140128) of the Comisión Nacional de Invest. Científica y Tecnológica, Chile.Martínez-Solano, FJ.; Iglesias Rey, PL.; Mora Meliá, D.; Fuertes-Miquel, VS. (2017). Exact skeletonization method in water distribution systems for hydraulic and quality models. Procedia Engineering. 186:286-293. https://doi.org/10.1016/j.proeng.2017.03.246S28629318

Pumping Station Design in Water Distribution Networks Considering the Optimal Flow Distribution between Sources and Capital and Operating Costs

[EN] The investment and operating costs of pumping stations in drinking water distribution networks are some of the highest public costs in urban sectors. Generally, these systems are designed based on extreme scenarios. However, in periods of normal operation, extra energy is produced, thereby generating excess costs. To avoid this problem, this work presents a new methodology for the design of pumping stations. The proposed technique is based on the use of a setpoint curve to optimize the operating and investment costs of a station simultaneously. According to this purpose, a novel mathematical optimization model is developed. The solution output by the model includes the selection of the pumps, the dimensions of pipelines, and the optimal flow distribution among all water sources for a given network. To demonstrate the advantages of using this technique, a case study network is presented. A pseudo-genetic algorithm (PGA) is implemented to resolve the optimization model. Finally, the obtained results show that it is possible to determine the full design and operating conditions required to achieve the lowest cost in a multiple pump station network.This work was supported by the Program Fondecyt Regular (Project N. 1210410) of the Agencia Nacional de Investigación y Desarrollo (ANID), Chile. It is also supported by CONICYT PFCHA/DOCTORADO BECAS CHILE/2018-21182013.Gutiérrez-Bahamondes, JH.; Mora-Meliá, D.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Salgueiro, Y. (2021). Pumping Station Design in Water Distribution Networks Considering the Optimal Flow Distribution between Sources and Capital and Operating Costs. Water. 13(21):1-14. https://doi.org/10.3390/w13213098S114132

Transient phenomena during the emptying process of a single pipe with water air interaction

[EN] Emptying pipelines can be critical in many water distribution networks because subatmospheric pressure troughs could cause considerable damage to the system due to the expansion of entrapped air. Researchers have given relatively little attention to emptying processes compared to filling processes. The intricacy of computations of this phenomenon makes it difficult to predict the behaviour during emptying, and there are only a few reliable models in the literature. In this work, a computational model for simulating the transient phenomena in single pipes is proposed, and was validated using experimental results. The proposed model is based on a rigid column to analyse water movement, the air¿water interface, and air pocket equations. Two practical cases were used to validate the model: (1) a single pipe with the upstream end closed, and (2) a single pipe with an air valve installed on the upstream end. The results show how the model accurately predicts the experimental data, including the pressure oscillation patterns and subatmospheric pressure troughs.This study was supported by the Program Fondecyt Regular [Project 1180660] of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile, http://data.crossref.org/fundingdata/funder/10.13039/501100002848.Fuertes-Miquel, VS.; Coronado-Hernández, OE.; Iglesias Rey, PL.; Mora Melia, D. (2019). Transient phenomena during the emptying process of a single pipe with water air interaction. Journal of Hydraulic Research. 57(3):318-326. https://doi.org/10.1080/00221686.2018.1492465S318326573Bashiri-Atrabi, H., & Hosoda, T. (2015). The motion of entrapped air cavities in inclined ducts. Journal of Hydraulic Research, 53(6), 814-819. doi:10.1080/00221686.2015.1060272Cabrera, E., Abreu, J., Pérez, R., & Vela, A. (1992). Influence of Liquid Length Variation in Hydraulic Transients. Journal of Hydraulic Engineering, 118(12), 1639-1650. doi:10.1061/(asce)0733-9429(1992)118:12(1639)Coronado-Hernández, O. E., Fuertes-Miquel, V. S., Iglesias-Rey, P. L., & Martínez-Solano, F. J. (2018). Rigid Water Column Model for Simulating the Emptying Process in a Pipeline Using Pressurized Air. Journal of Hydraulic Engineering, 144(4), 06018004. doi:10.1061/(asce)hy.1943-7900.0001446Fuertes-Miquel, V. S., López-Jiménez, P. A., Martínez-Solano, F. J., & López-Patiño, G. (2016). Numerical modelling of pipelines with air pockets and air valves. Canadian Journal of Civil Engineering, 43(12), 1052-1061. doi:10.1139/cjce-2016-0209Guinot, V. (2001). The discontinuous profile method for simulating two-phase flow in pipes using the single component approximation. International Journal for Numerical Methods in Fluids, 37(3), 341-359. doi:10.1002/fld.177Hou, Q., Tijsseling, A. S., Laanearu, J., Annus, I., Koppel, T., Bergant, A., … van ’t Westende, J. M. C. (2014). Experimental Investigation on Rapid Filling of a Large-Scale Pipeline. Journal of Hydraulic Engineering, 140(11), 04014053. doi:10.1061/(asce)hy.1943-7900.0000914Izquierdo, J., Fuertes, V. S., Cabrera, E., Iglesias, P. L., & Garcia-Serra, J. (1999). Pipeline start-up with entrapped air. Journal of Hydraulic Research, 37(5), 579-590. doi:10.1080/00221689909498518Laanearu, J., Annus, I., Koppel, T., Bergant, A., Vučković, S., Hou, Q., … van’t Westende, J. M. C. (2012). Emptying of Large-Scale Pipeline by Pressurized Air. Journal of Hydraulic Engineering, 138(12), 1090-1100. doi:10.1061/(asce)hy.1943-7900.0000631Leon, A. S., Ghidaoui, M. S., Schmidt, A. R., & Garcia, M. H. (2010). A robust two-equation model for transient-mixed flows. Journal of Hydraulic Research, 48(1), 44-56. doi:10.1080/00221680903565911Liou, C. P., & Hunt, W. A. (1996). Filling of Pipelines with Undulating Elevation Profiles. Journal of Hydraulic Engineering, 122(10), 534-539. doi:10.1061/(asce)0733-9429(1996)122:10(534)Liu, D., Zhou, L., Karney, B., Zhang, Q., & Ou, C. (2011). Rigid-plug elastic-water model for transient pipe flow with entrapped air pocket. Journal of Hydraulic Research, 49(6), 799-803. doi:10.1080/00221686.2011.621740Malekpour, A., & Karney, B. (2014). Column separation and rejoinder during rapid pipeline filling induced by a partial flow blockage. Journal of Hydraulic Research, 52(5), 693-704. doi:10.1080/00221686.2014.905502Martins, S. C., Ramos, H. M., & Almeida, A. B. (2015). Conceptual analogy for modelling entrapped air action in hydraulic systems. Journal of Hydraulic Research, 53(5), 678-686. doi:10.1080/00221686.2015.1077353Pozos, O., Gonzalez, C. A., Giesecke, J., Marx, W., & Rodal, E. A. (2010). Air entrapped in gravity pipeline systems. Journal of Hydraulic Research, 48(3), 338-347. doi:10.1080/00221686.2010.481839Tijsseling, A. S., Hou, Q., Bozkuş, Z., & Laanearu, J. (2015). Improved One-Dimensional Models for Rapid Emptying and Filling of Pipelines. Journal of Pressure Vessel Technology, 138(3). doi:10.1115/1.4031508Wang, K.-H., Shen, Q., & Zhang, B. (2003). Modeling propagation of pressure surges with the formation of an air pocket in pipelines. Computers & Fluids, 32(9), 1179-1194. doi:10.1016/s0045-7930(02)00103-2Wang, H., Zhou, L., Liu, D., Karney, B., Wang, P., Xia, L., … Xu, C. (2016). CFD Approach for Column Separation in Water Pipelines. Journal of Hydraulic Engineering, 142(10), 04016036. doi:10.1061/(asce)hy.1943-7900.0001171Zhou, L., & Liu, D. (2013). Experimental investigation of entrapped air pocket in a partially full water pipe. Journal of Hydraulic Research, 51(4), 469-474. doi:10.1080/00221686.2013.785985Zhou, L., Liu, D., & Karney, B. (2013). Investigation of Hydraulic Transients of Two Entrapped Air Pockets in a Water Pipeline. Journal of Hydraulic Engineering, 139(9), 949-959. doi:10.1061/(asce)hy.1943-7900.0000750Zhou, L., Liu, D., Karney, B., & Wang, P. (2013). Phenomenon of White Mist in Pipelines Rapidly Filling with Water with Entrapped Air Pockets. Journal of Hydraulic Engineering, 139(10), 1041-1051. doi:10.1061/(asce)hy.1943-7900.000076

Urban Drainage Network Rehabilitation Considering Storm Tank Installation and Pipe Substitution

[EN] The drainage networks of our cities are currently experiencing a growing increase in runoff flows, caused mainly by the waterproofing of the soil and the effects of climate change. Consequently, networks originally designed correctly must endure floods with frequencies much higher than those considered in the design phase. The solution of such a problem is to improve the network. There are several ways to rehabilitate a network: conduit substitution as a former method or current methods such as storm tank installation or combined use of conduit substitution and storm tank installation. To find an optimal solution, deterministic or heuristic optimization methods are used. In this paper, a methodology for the rehabilitation of these drainage networks based on the combined use of the installation of storm tanks and the substitution of some conduits of the system is presented. For this, a cost-optimization method and a pseudo-genetic heuristic algorithm, whose efficiency has been validated in other fields, are applied. The Storm Water Management Model (SWMM) model for hydraulic analysis of drainage and sanitation networks is used. The methodology has been applied to a sector of the drainage network of the city of Bogota in Colombia, showing how the combined use of storm tanks and conduits leads to lower cost rehabilitation solutions.This work was supported by the Program Fondecyt Regular (Project 1180660) of the Comision Nacional de Investigación Científica y Tecnológica (Conicyt), Chile.Ngamalieu-Nengoue, UA.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Mora-Meliá, D.; Saldarriaga, J. (2019). Urban Drainage Network Rehabilitation Considering Storm Tank Installation and Pipe Substitution. Water. 11(3):515-537. https://doi.org/10.3390/w11030515S51553711

Reducción del espacio de búsqueda para un problema de optimización de diseño de estaciones de bombeo

117 p.Diversos investigadores se dedican a la optimización de redes de agua potable, problemas complejos para los cuales recurren a las metaheurísticas de optimización. Ahora bien, independiente del algoritmo metaheurístico a emplear, siempre el factor común será el dominio de las variables del sistema con el cual trabajarán. Específicamente, para el problema de diseño económico de estaciones de bombeo, una de las principales variables de decisión, es la cantidad de caudal que entregan las estaciones. Por esta razón, reducir el dominio de estas variables puede generar grandes beneficios en los problemas de optimización en redes de distribución de agua potable. Existen indicios, de que se pueden reducir los dominios del problema de optimización, acotando los rangos operacionales de las estaciones de bombeo, estableciendo los máximos y mínimos caudales que pueden entregar y con ello lograr una convergencia a soluciones óptimas con una menor cantidad de iteraciones en la función objetivo del problema de optimización. El objetivo principal de esta memoria será desarrollar una metodología para hacer un preprocesamiento de datos y busque reducir los dominios de los caudales entregados por cada estación de bombeo. Para poder desarrollar dicha metodología, se presentan casos de estudios reales a los cuales se les aplicará un problema de optimización propuesto por Gutiérrez (2021), con y sin la metodología propuesta, con el fin de poder validarla. Para lo anterior, también se establecerán distintos indicadores, como el porcentaje reducción del dominio y la cantidad de iteraciones del problema de optimización para converger a soluciones óptimas. Dentro de los principales resultados, se encuentran variaciones muy dispersas de la reducción de dominio, las cuales van desde 37% hasta los 90% de reducción. Finalmente, se demuestra en esta memoria, que la metodología permite una efectiva reducción del dominio de las variables de decisión, y tiene directa relación con la cantidad de estaciones que operen en una red de agua potable. // ABSTRACT: Various researchers are dedicated to the optimization of drinking water networks, complex problems for which they resort to optimization metaheuristics. Now, regardless of the metaheuristic algorithm to be used, the common factor will always be the domain of the variables of the system with which they will work. Specifically, for the problem of economic design of pumping stations, one of the main decision variables is the amount of flow that the stations deliver. For this reason, reducing the domain of these variables can generate great benefits in optimization problems in drinking water distribution networks. There are indications that the domains of the optimization problem can be reduced, narrowing the operational ranges of the pumping stations, establishing the maximum and minimum flow rates that they can deliver and thus achieve a convergence to optimal solutions with fewer iterations in the objective function of the optimization problem. For this reason, the main objective of this report will be to develop a methodology to pre-process data and seek to reduce the domains of the flows delivered by each pumping station. To develop this methodology, real case studies are presented to which an optimization problem proposed by Gutiérrez (2021) will be applied, with and without the proposed methodology, in order to validate it. For the above, different indicators will also be established, such as the percentage reduction of the domain and the number of iterations of the optimization problem to converge to optimal solutions. Among the main results, there are widely dispersed variations in domain reduction, ranging from 37% to 90% reduction. Finally, it is demonstrated in this report that the methodology allows an effective reduction of the domain of the decision variables and is directly related to the number of stations that operate in a drinking water network

Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization

The Storm Water Management Model (SWMM) is a dynamic simulation engine of flow in sewer systems developed by the USEPA. It has been successfully used for analyzing and designing both storm water and waste water systems. However, despite including some interfacing functions, these functions are insufficient for certain simulations. This paper describes some new functions that have been added to the existing ones to form a library of functions (Toolkit). The Toolkit presented here will allow the direct modification of network data during simulation without the need to access the input file. To support the use of this library, a testing protocol was performed in order to evaluate both calculation time and accuracy of results. Finally, a case study is presented. In this application, this library will be used for the design of a sewerage network by using a genetic algorithm based on successive iterations.The authors would like to thank the Colombian company PAVCO-MEXICHEM and the Colombian Administrative Department for Science, Technology and Innovation COLCIENCIAS, for financing the "Drenaje Urbano y Cambio Climatico: Hacia los Sistemas de Drenaje Urbano del Futuro" investigation, under which the present paper was conceived. Likewise, the development of this paper has been possible thanks to the Spanish Ministry for Science and Innovation, who covered the "DPI2009-13674 OPERAGUA: Mejora de las tecnicas de llenado y operacion de redes de abastecimiento de agua" research project.Martínez-Solano, FJ.; Iglesias Rey, PL.; Saldarriaga, JG.; Vallejo, D. (2016). Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization. Water. 8(6). doi:10.3390/w8060259S86Mays, L. W., & Yen, B. C. (1975). Optimal cost design of branched sewer systems. Water Resources Research, 11(1), 37-47. doi:10.1029/wr011i001p00037Elimam, A. A., Charalambous, C., & Ghobrial, F. H. (1989). Optimum Design of Large Sewer Networks. Journal of Environmental Engineering, 115(6), 1171-1190. doi:10.1061/(asce)0733-9372(1989)115:6(1171)Van Nooijen, R. R., & Kolechkina, A. (2013). Speed of discrete optimization solvers for real time sewer control. Urban Water Journal, 10(5), 354-363. doi:10.1080/1573062x.2013.820330Banik, B. K., Di Cristo, C., & Leopardi, A. (2014). SWMM5 Toolkit Development for Pollution Source Identification in Sewer Systems. Procedia Engineering, 89, 750-757. doi:10.1016/j.proeng.2014.11.503Moeini, R., & Afshar, M. H. (2013). Constrained Ant Colony Optimisation Algorithm for the layout and size optimisation of sanitary sewer networks. Urban Water Journal, 10(3), 154-173. doi:10.1080/1573062x.2012.716445Krebs, G., Kokkonen, T., Valtanen, M., Koivusalo, H., & Setälä, H. (2013). A high resolution application of a stormwater management model (SWMM) using genetic parameter optimization. Urban Water Journal, 10(6), 394-410. doi:10.1080/1573062x.2012.739631Savić, D. A., Bicik, J., & Morley, M. S. (2011). A DSS generator for multiobjective optimisation of spreadsheet-based models. Environmental Modelling & Software, 26(5), 551-561. doi:10.1016/j.envsoft.2010.11.004Marchi, A., & Simpson, A. R. (2013). Correction of the EPANET Inaccuracy in Computing the Efficiency of Variable Speed Pumps. Journal of Water Resources Planning and Management, 139(4), 456-459. doi:10.1061/(asce)wr.1943-5452.0000273Rossman, L. A., Dickinson, R. E., Schade, T., Chan, C. C., … Burgess, E. (2004). SWMM 5 - the Next Generation of EPA’s Storm Water Management Model. Journal of Water Management Modeling. doi:10.14796/jwmm.r220-16Yazdi, J., Sadollah, A., Lee, E. H., Yoo, D. G., & Kim, J. H. (2015). Application of multi-objective evolutionary algorithms for the rehabilitation of storm sewer pipe networks. Journal of Flood Risk Management, 10(3), 326-338. doi:10.1111/jfr3.12143Mays, L. W., & Wenzel, H. G. (1976). Optimal design of multilevel branching sewer systems. Water Resources Research, 12(5), 913-917. doi:10.1029/wr012i005p00913Afshar, M. H., & Rohani, M. (2012). Optimal design of sewer networks using cellular automata-based hybrid methods: Discrete and continuous approaches. Engineering Optimization, 44(1), 1-22. doi:10.1080/0305215x.2011.557071Cozzolino, L., Cimorelli, L., Covelli, C., Mucherino, C., & Pianese, D. (2015). An Innovative Approach for Drainage Network Sizing. Water, 7(12), 546-567. doi:10.3390/w7020546Guo, Y. F., Walters, G. A., Khu, S. T., & Keedwell, E. C. (2008). Efficient Multiobjective Storm Sewer Design Using Cellular Automata and Genetic Algorithm Hybrid. Journal of Water Resources Planning and Management, 134(6), 511-515. doi:10.1061/(asce)0733-9496(2008)134:6(511)Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., & Fuertes-Miquel, V. S. (2013). Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators. Water Resources Management, 27(12), 4149-4162. doi:10.1007/s11269-013-0400-6Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., & Ballesteros-Pérez, P. (2015). Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing. Water Resources Management, 29(13), 4817-4831. doi:10.1007/s11269-015-1092-xElbeltagi, E., Hegazy, T., & Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, 19(1), 43-53. doi:10.1016/j.aei.2005.01.00