Trade-off between Total Cost and Reliability for Anytown Water Distribution Network

Farmani, Raziyeh; Walters, Godfrey A.; Savić, Dragan

doi:10.1061/(asce)0733-9496(2005)131:3(161)

Cited by 243 publications

(114 citation statements)

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“…Farmani et al (2005Farmani et al ( , 2006 considered the resilience index (Todini 2000) and water age (Rossman 2002). Prasad and Tanyimboh (2008) investigated the statistical flow entropy (Tanyimboh and Templeman 2000;Tanyimboh et al 2011) and resilience index.…”

Section: Introductionmentioning

confidence: 99%

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

Siew

Tanyimboh

Seyoum

2016

Water Resour Manage

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This paper describes the development and application of a new multiobjective evolutionary optimization approach for the design and upgrading of water distribution systems with multiple pumps and service reservoirs. The optimization model employs a pressure-driven analysis simulator that accounts for the minimum node pressure constraints and conservation of mass and energy. Pump scheduling, tank siting and tank design are integrated seamlessly in the optimization without introducing additional heuristic procedures. The computational solution of the optimization problem is entirely penalty-free, thanks to pressure-driven analysis and the inclusion of explicit criteria for tank depletion and replenishment. The model was applied to the Anytown network that is a benchmark optimization problem. Many new solutions were achieved that are cheaper and offer superior performance compared to previous solutions in the literature. Detailed and extensive simulations of the solutions achieved were carried out. Spatial and temporal variations in water quality were investigated by simulating the chlorine residual and disinfection by-products in addition to water age. The hydraulic requirements were satisfied; efficiency of pumps was consistently high; effective operation of the new and existing tanks was achieved; water quality was improved; and overall computational efficiency was high. The formulation is entirely generic.Keywords Demand-driven analysis . Pressure-driven analysis . Penalty-free constrained multiobjective evolutionary optimization . Water distribution system . Optimal pump scheduling . Service reservoir design and operation Water Resour Manage (2016) 30:3671-3688

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Section: Introductionmentioning

confidence: 99%

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

Siew

Tanyimboh

Seyoum

2016

Water Resour Manage

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“…For engineering systems, reliability is generally defined as the probability that a system performs its mission within specified limits for a given period of time in a specified environment (Bazovski, 1961, as cited in Cullinane et aI., 1992. With respect to water distribution systems, Farmani et al (2005) suggests that reliability is the ability of the network to provide consumers with adequate and high-quality supply even under abnormal conditions.…”

Section: Reliability Considerationsmentioning

confidence: 99%

“…Similar to the lack of an universal definition of water distribution system reliability, it was identified that there was no universal measure of reliability. Farmani et al (2005) cites a paper by Walski et al (1987) which suggests that a reliability measure should reflect the way in which water users are affected, such as the number of users with limited or no service and the length of time for which the event occurs. In addition, Goulter (1995) proposes that the reliability measure should include both the probability of the event causing failure or loss of service, as well as the magnitude of impact of the failure.…”

Section: T O W Nmentioning

confidence: 99%

Optimal Reliability-Based Design of Bulk Water Supply Systems

Chang

Zyl

2014

J. Water Resour. Plann. Manage.

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DJ :;sc rt(l 1I UIl s uhnlltt ed In p a rt i~l fulfi lm en t o~' th e reqlllrcrnen ts fu r tht., degree ofM~s t e r ofSClence In Enginee ring AbstractBulk water supply systems are usually designed according to deterministic design guidelines. In South Africa, design guidelines specify that a bulk storage reservoir should have a storage capacity of 48 hours of annual average daily demand (AADD), and the feeder pipe a capacity of 1.5 times AADD (CSIR, 2000). Nel & Haarhoff (1996) proposed a stochastic analysis method that allowed the reliability of a reservoir to be estimated based on a Monte Carlo analysis of consumer demand, fire water demand and pipe failures. Van Zyl et al. (2008) developed this method further and proposed a design criterion of one failure in ten years under seasonal peak conditions.In this study, a method for the optimal design of bulk water supply systems is proposed with the design variables being the configuration of the feeder pipe system, the feeder pipe diameters (i.e. capacity), and the size of the bulk storage reservoir. The stochastic analysis method is applied to determine a trade-off curve between system cost and reliability, from which the designer can select a suitable solution.Optimisation of the bulk system was performed using the multi-objective genetic algorithm, NSGA-II. As Monte Carlo sampling can be computationally expensive, especially when large numbers of simulations are required in an optimisation exercise, a compression heuristic was implemented and refined to reduce the computational effort required of the stochastic simulation. Use of the compression heuristic instead of full Monte Carlo simulation in the reliability analysis achieved computational time savings of around 75% for the optimisation of a typical system.Application of the optimisation model showed that it was able to successfully produce a set of Pareto-optimal solutions ranging from low reliability, low cost solutions to high reliability, high cost solutions. The proposed method was first applied to a typical system, resulting in an optimal reservoir size of approximately 22 h AADD and feeder pipe capacity of 2 times AADD. This solution achieved 9% savings in total system cost compared to the South African design guidelines. In addition, the optimal solution proved to have better reliability that one designed according to South African guidelines.A sensitivity analysis demonstrated the effects of changing various system and stochastic parameters from typical to low and high values. The sensitivity results revealed that the length of the feeder pipe system has the greatest impact on both the cost and reliability of the bulk system. It was also found that a single feeder pipe is optimal in most cases, and that parallel feeder pipes are only optimal for short feeder pipe lengths.The optimisation model is capable of narrowing down the search region to a handful of possible design solutions, and can thus be used by the engineer as a tool to assist with the design of the final system.

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“…There can be several approaches to it: The benefit from solution X can be determined as the difference between the predicted deficiencies of the initial unimproved system and those of solution X, estimated as a scaled and weighted sum of deficiency measures. Deficiency measures usually include hydraulic parameters, such as pressure and storage shortfalls (Walters et al, 1999, Prasad andPark, 2004), although other measures of benefit, such as reliability can be taken into account (Farmani et al, 2005). …”

Section: Multiobjective Optimization Of Water Distribution Networkmentioning

confidence: 99%

“…Previous approaches in literature include 6-hour time step (Murphy et al, 1994, Walters et al, 1999 and 1hour time step (Farmani et al, 2005), with additional computational assumptions, as follows:…”

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confidence: 99%

Tank Simulation for the Optimization of Water Distribution Networks

Vamvakeridou-Lyroudia

Savić

Walters

2007

J. Hydraul. Eng.

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In this paper an original approach to the simulation of floating-on-the-system tanks as decision variables for water distribution system design optimization is presented, aiming to bridge the gap between traditional engineering practice and mathematical considerations needed for genetic algorithms. The paper includes a systematic and detailed critical overview of various mathematical approaches in literature, as well as a novel, more "engineering oriented" approach to the simulation of tanks as decision variables for water distribution system design optimization, describing in detail assumptions and impacts to the evaluation of potential solutions. Tank simulation is based on two decision variables: capacity and minimum normal operational level, omitting risers. Shape and ratio between emergency/total capacities are taken into consideration as design parameters. Assessment of tank performance is carried out by four criteria for the normal daily operational cycle, differentiating between operational and filling capacity, as well as two further criteria for emergency flows. The original design and operational mathematical assumptions are implemented in a fuzzy multiobjective GA model, which is applied to the well-known example from literature "Anytown" water distribution network to benchmark the results.Subject Heading List to the CEDB fuzzy sets, optimization models, multiple objective analysis, design, water distribution, pipe networks, water tanks

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Trade-off between Total Cost and Reliability for Anytown Water Distribution Network

Cited by 243 publications

References 11 publications

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

Optimal Reliability-Based Design of Bulk Water Supply Systems

Tank Simulation for the Optimization of Water Distribution Networks

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