Optimal Design of Water Distribution Network under Hydraulic Uncertainties

Dongre, Shilpa; Gupta, Rajesh

doi:10.1061/ajrua6.0000903

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…Sivakumar et al (2016) studied the uncertainties in the tube rugosity and evaluated the tube flowrate and the different pressures between two adjacent nodes. Dongre and Gupta (2017) considered uncertainties in the water demand and in the tube rugosity using fuzzy logic. Calvo et al (2018) considered non-correlated functions of log-normal probability distributions in the management of valves.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The objective presented in Branisavljevic et al (2009) was to reduce nodal uncertainties using flow data in modeling the problem. Dongre and Gupta (2017) considered uncertainties in water demand and pipe roughness to consider various levels of pressure acceptance. Salcedo-Díaz et al (2020) obtained a result similar to that provided here, but the authors did not use the flow direction as a variable in the problem.…”

Section: Case Studymentioning

confidence: 99%

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Water Distribution Networks Optimization Considering Uncertainties in the Demand Nodes

Cassiolato,

Ruiz-Femenia,

Salcedo-Diaz

et al. 2024

Water Resour Manage

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The fluctuation in the consumption of treated water is a situation that distribution networks gradually face. In times of greater demand, this consumption tends to suffer unnecessary impacts due to the lack of water. The uncertainty that occurs in water consumption can be mathematically modeled by a finite set of scenarios generated by a normal distribution and attributed to the network design. This study presents an optimization model to minimize network installation and operation costs under uncertainties in water demands. A Mixed Integer Nonlinear Programming model is proposed, considering the water flow directions in the pipes as unknown. A deterministic approach is used to solve the problem in three steps: First, the problem is solved with a nominal value for each uncertain parameter. In the second stage, the problem is solved for all scenarios, with the independent variables of the scenario being fixed and obtained from the solution reached in the first stage, known as the deterministic solution. Finally, all scenarios are solved without fixing any variable values, in a stochastic approach. Two case studies were used to test the applicability of the model and global optimization techniques were used to solve the problem. The results show that the stochastic solution can lead to optimal solutions for robust and flexible water distribution networks, capable of working under different conditions, considering the uncertainties of node demand and variable pipe directions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Water Distribution Networks Optimization Considering Uncertainties in the Demand Nodes

Cassiolato,

Ruiz-Femenia,

Salcedo-Diaz

et al. 2024

Water Resour Manage

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“…A system of nonlinear equations is used to mimic the hydraulic dynamics inside a pressured, looping pipe network. The energy and continuity equations are considered simultaneously for obtaining the solution to these equations, along with a head loss function [33]. The cluster analysis provided by CiteSpace detected the cluster labels.…”

Section: Optimal Design Of Water Distribution Systemsmentioning

confidence: 99%

Optimization of Water Distribution Systems Using Genetic Algorithms: A Review

Parvaze

Kumar

Khan

et al. 2023

Arch Computat Methods Eng

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Water distribution networks are crucial for supplying consumers with quality and adequate water. A water distribution system comprises connected hydraulic components which ensure water supply and distribution to meet demand. Optimization of water distribution networks is carried out to minimize resource utilization and expenditure or maximize the system’s efficiency and higher benefits. Genetic algorithms signify an effective search technique for non-linear optimization problems and have gained acceptance among water resources planners and managers. This paper reviews various developments in the optimization of water distribution systems using the technique of genetic algorithms. These developments are pertinent to creating novel systems for distributing water and the expansion, reinforcement, and rehabilitation process for prevailing water supply mechanisms. Graphical Abstract

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“…The WDN's nodes demand uncertainty can be quantified using trapezoidal or triangular membership functions (Dongre and Gupta 2017). In the triangular membership function, the most probable value of the nodes demand is only the crisp value (𝑞 𝑐𝑟𝑖𝑠𝑝 ), while in the trapezoidal membership function, the most probable values of the nodes demand are in an interval between the minimum and maximum demand values.…”

Section: Noes Demand Fuzzy Membership Functionmentioning

confidence: 99%

“…In the triangular membership function, the most probable value of the nodes demand is only the crisp value (𝑞 𝑐𝑟𝑖𝑠𝑝 ), while in the trapezoidal membership function, the most probable values of the nodes demand are in an interval between the minimum and maximum demand values. Various researchers such as Revelli and Ridolfi 2002;Maskey et al 2004;Nemanja 2006;Dongre and Gupta 2017;Geranmehr et al 2019 have considered triangular membership functions for the nodes demand. In this research, according to the observed data of the studied WDN's nodes demand, the triangular membership function used to quantify the WDN's nodes demand.…”

Section: Noes Demand Fuzzy Membership Functionmentioning

confidence: 99%

Uncertainty Analysis of WDN Pipes Repair and Replacement Optimal Instruction Using EPANET Simulation Model and Fuzzy Cut Approach Combination

Jafari

Zahiri

Hadad

et al. 2021

Preprint

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Optimization of the initial design or pipes repair and replacement instructions of water distribution network during operation period is based on the crisp values of the input variables. Some input variables such as node demand, pipe roughness coefficient and reservoir water level have the uncertain nature. Changing the input parameters values during operation period, due to uncertainty, changes the water distribution network behavior and performance compared to the crisp input parameters values. Recognition and analyzing these behaviors are very important to make the right decision to deal with their consequences and reduce the water distribution network problems, during operation period. In this research, the water distribution network nodes pressure uncertainty due to the pipe's roughness coefficient and the nodes demand uncertainty as input parameters, is analyzed after the implementation of the optimal pipes repair and replacement instruction. For the purpose, a combination of simulation model (EPANET) and fuzzy α-cut approach is used. Fuzzy membership functions of the input and output variables are selected as triangular type and the membership functions values, after normalization, were in the range of zero and one. The extreme values combinations of two uncertain input variables, at each uncertainty level, in the form of four scenarios, were considered as the input fuzzy set of the simulation model. Between the research scenarios, the second scenario, which is the combination of the minimum pipe roughness coefficient and the maximum demand, is known as the critical scenario. The research results show that in the critical scenario, at the highest uncertainty level, the water distribution network reliability index is very low and about 30 to 40% due to lack of required pressure of the most nodes. At the uncertainty lower levels, the reliability index rises above 75%, which is relatively acceptable. In the first and fourth scenarios, the network reliability index is always more than 72%. In the third scenario, the network reliability index, at all uncertainty levels, is always more than 90%.

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Optimal Design of Water Distribution Network under Hydraulic Uncertainties

Cited by 11 publications

References 21 publications

Water Distribution Networks Optimization Considering Uncertainties in the Demand Nodes

Water Distribution Networks Optimization Considering Uncertainties in the Demand Nodes

Optimization of Water Distribution Systems Using Genetic Algorithms: A Review

Uncertainty Analysis of WDN Pipes Repair and Replacement Optimal Instruction Using EPANET Simulation Model and Fuzzy Cut Approach Combination

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