Rehabilitation of a water distribution system using sequential multiobjective optimization models

Identification of the optimal rehabilitation plan for a large water distribution system (WDS) with a substantial number of decision variables is a challenging task, especially when no supercomputer facilities are available. This paper presents an initiative methodology for the rehabilitation of WDS based on three sequential stages of multiobjective optimization models for gradually identifying the best-known Pareto front (PF). A two-objective optimization model is used in the first two stages where the objectives are to minimize rehabilitated infrastructure costs and operational costs. The optimization model in the first stage applies to a skeletonized WDS. The PFs obtained in Stage 1 are further improved in Stage 2 using the same two-objective optimization problem but for the full network. The third stage employs a three-objective optimization model by minimizing the cost of additional pressure reducing valves (PRVs) as the third objective. The suggested methodology was demonstrated through use of a real and large WDS from the literature. Results show the efficiency of the suggested methodology to achieve the optimal solutions for a large WDS in a reasonable computational time. Results also suggest the minimum total costs that will be obtained once maximum leakage reduction is achieved due to maximum possible pipeline rehabilitation without increasing the existing tanks

Competent genetic-evolutionary optimization of water distribution systems

A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms - a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms

Multiobjective optimization of water distribution systems accounting for economic cost, hydraulic reliability, and greenhouse gas emissions

In this paper, three objectives are considered for the optimization of water distribution systems (WDSs): the traditional objectives of minimizing economic cost and maximizing hydraulic reliability and the recently proposed objective of minimizing greenhouse gas (GHG) emissions. It is particularly important to include the GHG minimization objective for WDSs involving pumping into storages or water transmission systems (WTSs), as these systems are the main contributors of GHG emissions in the water industry. In order to better understand the nature of tradeoffs among these three objectives, the shape of the solution space and the location of the Pareto-optimal front in the solution space are investigated for WTSs and WDSs that include pumping into storages, and the implications of the interaction between the three objectives are explored from a practical design perspective. Through three case studies, it is found that the solution space is a U-shaped curve rather than a surface, as the tradeoffs among the three objectives are dominated by the hydraulic reliability objective. The Pareto-optimal front of real-world systems is often located at the "elbow" section and lower "arm" of the solution space (i.e., the U-shaped curve), indicating that it is more economic to increase the hydraulic reliability of these systems by increasing pipe capacity (i.e., pipe diameter) compared to increasing pumping power. Solutions having the same GHG emission level but different cost-reliability tradeoffs often exist. Therefore, the final decision needs to be made in conjunction with expert knowledge and the specific budget and reliability requirements of the system. © 2013. American Geophysical Union. All Rights Reserved.Wenyan Wu, Holger R. Maier, and Angus R. Simpso

Optimal design of water distribution systems based on entropy and topology

A new multi-objective evolutionary optimization approach for joint topology and pipe size design of water distribution systems is presented. The algorithm proposed considers simultaneously the adequacy of flow and pressure at the demand nodes; the initial construction cost; the network topology; and a measure of hydraulic capacity reliability. The optimization procedure is based on a general measure of hydraulic performance that combines statistical entropy, network connectivity and hydraulic feasibility. The topological properties of the solutions are accounted for and arbitrary assumptions regarding the quality of infeasible solutions are not applied. In other words, both feasible and infeasible solutions participate in the evolutionary processes; solutions survive and reproduce or perish strictly according to their Pareto-optimality. Removing artificial barriers in this way frees the algorithm to evolve optimal solutions quickly. Furthermore, any redundant binary codes that result from crossover or mutation are eliminated gradually in a seamless and generic way that avoids the arbitrary loss of potentially useful genetic material and preserves the quality of the information that is transmitted from one generation to the next. The approach proposed is entirely generic: we have not introduced any additional parameters that require calibration on a case-by-case basis. Detailed and extensive results for two test problems are included that suggest the approach is highly effective. In general, the frontier-optimal solutions achieved include topologies that are fully branched, partially- and fully-looped and, for networks with multiple sources, completely separate sub-networks

Trunk Network Rehabilitation for Resilience Improvement and Energy Recovery in Water Distribution Networks

[EN] Water distribution networks (WDNs) are designed to meet water demand with minimum implementation costs. However, this approach leads to poor long-term results, since system resilience is also minimal, and this requires the rehabilitation of the network if the network is expanded or the demand increases. In addition, in emergency situations, such as pipe bursts, large areas will suffer water shortage. However, the use of resilience as a criterion for WDN design is a difficult task, since its economic value is subjective. Thus, in this paper, it is proposed that trunk networks (TNs) are rehabilitated when considering the generation of electrical energy using pumps as turbines (PATs) to compensate for an increase of resilience derived from increasing pipe diameters. During normal operation, these micro-hydros will control pressure and produce electricity. When an emergency occurs, a by-pass can be used to increase network pressure. The results that were obtained for two hypothetical networks show that a small increase in TN pipe diameters is sufficient to significantly improve the resilience of the WDN. In addition, the value of the energy produced surpasses the investment that is made during rehabilitation.The authors wish to thank the project REDAWN (Reducing Energy Dependency in Atlantic Area Water Networks) EAPA_198/2016 from INTERREG ATLANTIC AREA PROGRAMME 2014-2020.Meirelles, G.; Brentan, BM.; Izquierdo Sebastián, J.; Ramos, HM.; Luvizotto, E. (2018). Trunk Network Rehabilitation for Resilience Improvement and Energy Recovery in Water Distribution Networks. Water. 10(6):1-14. https://doi.org/10.3390/w10060693S114106Zong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Maier, H. R., Simpson, A. R., Zecchin, A. C., Foong, W. K., Phang, K. Y., Seah, H. Y., & Tan, C. L. (2003). Ant Colony Optimization for Design of Water Distribution Systems. Journal of Water Resources Planning and Management, 129(3), 200-209. doi:10.1061/(asce)0733-9496(2003)129:3(200)Suribabu, C. R., & Neelakantan, T. R. (2006). Design of water distribution networks using particle swarm optimization. Urban Water Journal, 3(2), 111-120. doi:10.1080/15730620600855928Baños, R., Reca, J., Martínez, J., Gil, C., & Márquez, A. L. (2011). Resilience Indexes for Water Distribution Network Design: A Performance Analysis Under Demand Uncertainty. Water Resources Management, 25(10), 2351-2366. doi:10.1007/s11269-011-9812-3Shokoohi, M., Tabesh, M., Nazif, S., & Dini, M. (2016). Water Quality Based Multi-objective Optimal Design of Water Distribution Systems. Water Resources Management, 31(1), 93-108. doi:10.1007/s11269-016-1512-6Marques, J., Cunha, M., & Savić, D. (2015). Using Real Options in the Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 141(2), 04014052. doi:10.1061/(asce)wr.1943-5452.0000448Schwartz, R., Housh, M., & Ostfeld, A. (2016). Least-Cost Robust Design Optimization of Water Distribution Systems under Multiple Loading. Journal of Water Resources Planning and Management, 142(9), 04016031. doi:10.1061/(asce)wr.1943-5452.0000670Giustolisi, O., Laucelli, D., & Colombo, A. F. (2009). Deterministic versus Stochastic Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 135(2), 117-127. doi:10.1061/(asce)0733-9496(2009)135:2(117)Lansey, K. E., Duan, N., Mays, L. W., & Tung, Y. (1989). Water Distribution System Design Under Uncertainties. Journal of Water Resources Planning and Management, 115(5), 630-645. doi:10.1061/(asce)0733-9496(1989)115:5(630)Zheng, F., Simpson, A., & Zecchin, A. (2015). Improving the efficiency of multi-objective evolutionary algorithms through decomposition: An application to water distribution network design. Environmental Modelling & Software, 69, 240-252. doi:10.1016/j.envsoft.2014.08.022Geem, Z. (2015). Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search. Water, 7(12), 3613-3625. doi:10.3390/w7073613Prasad, T. D., & Park, N.-S. (2004). Multiobjective Genetic Algorithms for Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 130(1), 73-82. doi:10.1061/(asce)0733-9496(2004)130:1(73)Pérez-Sánchez, M., Sánchez-Romero, F., Ramos, H., & López-Jiménez, P. (2017). Energy Recovery in Existing Water Networks: Towards Greater Sustainability. Water, 9(2), 97. doi:10.3390/w9020097De Marchis, M., & Freni, G. (2015). Pump as turbine implementation in a dynamic numerical model: cost analysis for energy recovery in water distribution network. Journal of Hydroinformatics, 17(3), 347-360. doi:10.2166/hydro.2015.018Carravetta, A., del Giudice, G., Fecarotta, O., & Ramos, H. (2013). PAT Design Strategy for Energy Recovery in Water Distribution Networks by Electrical Regulation. Energies, 6(1), 411-424. doi:10.3390/en6010411Lima, G. M., Luvizotto, E., Brentan, B. M., & Ramos, H. M. (2018). Leakage Control and Energy Recovery Using Variable Speed Pumps as Turbines. Journal of Water Resources Planning and Management, 144(1), 04017077. doi:10.1061/(asce)wr.1943-5452.0000852Carravetta, A., Del Giudice, G., Fecarotta, O., & Ramos, H. M. (2012). Energy Production in Water Distribution Networks: A PAT Design Strategy. Water Resources Management, 26(13), 3947-3959. doi:10.1007/s11269-012-0114-1Lydon, T., Coughlan, P., & McNabola, A. (2017). Pump-As-Turbine: Characterization as an Energy Recovery Device for the Water Distribution Network. Journal of Hydraulic Engineering, 143(8), 04017020. doi:10.1061/(asce)hy.1943-7900.0001316Afshar, A., Jemaa, F. B., & Mariño, M. A. (1990). Optimization of Hydropower Plant Integration in Water Supply System. Journal of Water Resources Planning and Management, 116(5), 665-675. doi:10.1061/(asce)0733-9496(1990)116:5(665)Meirelles Lima, G., Brentan, B. M., & Luvizotto, E. (2018). Optimal design of water supply networks using an energy recovery approach. Renewable Energy, 117, 404-413. doi:10.1016/j.renene.2017.10.080Campbell, E., Izquierdo, J., Montalvo, I., Ilaya-Ayza, A., Pérez-García, R., & Tavera, M. (2015). A flexible methodology to sectorize water supply networks based on social network theory concepts and multi-objective optimization. Journal of Hydroinformatics, 18(1), 62-76. doi:10.2166/hydro.2015.146Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., & Santonastaso, G. F. (2017). Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures. Water Supply, 18(3), 767-777. doi:10.2166/ws.2017.124Bragalli, C., D’Ambrosio, C., Lee, J., Lodi, A., & Toth, P. (2011). On the optimal design of water distribution networks: a practical MINLP approach. Optimization and Engineering, 13(2), 219-246. doi:10.1007/s11081-011-9141-7Reca, J., & Martínez, J. (2006). Genetic algorithms for the design of looped irrigation water distribution networks. Water Resources Research, 42(5). doi:10.1029/2005wr004383Di Nardo, A., Di Natale, M., Santonastaso, G. F., Tzatchkov, V. G., & Alcocer-Yamanaka, V. H. (2014). Water Network Sectorization Based on Graph Theory and Energy Performance Indices. Journal of Water Resources Planning and Management, 140(5), 620-629. doi:10.1061/(asce)wr.1943-5452.0000364Hajebi, S., Temate, S., Barrett, S., Clarke, A., & Clarke, S. (2014). Water Distribution Network Sectorisation Using Structural Graph Partitioning and Multi-objective Optimization. Procedia Engineering, 89, 1144-1151. doi:10.1016/j.proeng.2014.11.238Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2), 115-122. doi:10.1016/s1462-0758(00)00049-2Brentan, B. M., Campbell, E., Meirelles, G. L., Luvizotto, E., & Izquierdo, J. (2017). Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization. Mathematical Problems in Engineering, 2017, 1-12. doi:10.1155/2017/9053238Lima, G. M., Luvizotto, E., & Brentan, B. M. (2017). Selection and location of Pumps as Turbines substituting pressure reducing valves. Renewable Energy, 109, 392-405. doi:10.1016/j.renene.2017.03.056Letting, L., Hamam, Y., & Abu-Mahfouz, A. (2017). Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization. Water, 9(8), 593. doi:10.3390/w908059

Lost in optimisation of water distribution systems? A literature review of system design

This is the final version of the article. Available from MDPI via the DOI in this record.Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details

Optimal design of water distribution systems using many-objective visual analytics

Copyright © 2013 American Society of Civil EngineersThis paper reports the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multiobjective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (εεε-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives, as follows: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age, and (6) fire-fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This paper demonstrates that a many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision-making in the WDS design process

Optimal operation of water distribution systems using a graph theory–based configuration of district metered areas

Optimal operation of large water distribution systems (WDS) has always been a tedious task especially when combined with determination of district metered areas (DMAs). This paper presents a novel framework based on graph theory and optimisation models to design DMA configuration and identify optimal operation of large WDS for both dry and rainy seasons. The methodology comprise three main phases of preliminary analysis, DMA configuration and optimal operation. The preliminary analysis assists in identifying key features and potential bottlenecks in the WDS and hence narrow down the large number of decision variables. The second phase employs a graph theory approach to specify DMAs and adjust their configuration based on similarity of total water demands and pressure uniformity in DMAs. Third phase uses several consecutive single-objective and multi-objective optimisation models. The decision variables are pipe rehabilitation, tank upgrade, location of valves and pipes closure, and valve settings for each DMA. The objective functions are to minimise total annual cost of rehabilitation, water age and pressure uniformity. The proposed methodology is demonstrated through its application to large real-world WDS of E-Town. The results show that the proposed methodology can determine a desirable DMA configuration mainly supplied directly by trunk mains