Evaluation of genetic algorithms using discrete and continuous methods for pump optimization of water distribution systems

Abkenar, Seyed Mohsen Sadatiyan; Stanley, Samuel Dustin; Miller, Carol J.; Chase, Donald V.; McElmurry, Shawn P.

doi:10.1016/j.suscom.2014.09.003

Cited by 26 publications

(16 citation statements)

References 7 publications

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“…Pollutant Emission Pump Station Optimization (PEPSO) is a platform developed by the water research team at Wayne State University for optimizing the pump schedule of the WDS [16]. The initial version of PEPSO used weighting factors to calculate a single combined objective from electricity usage, pollutant emissions and penalties [22]. However, the newer version of this tool is equipped with a multi-objective optimization algorithm to optimize each objective independent of others and find the Pareto frontiers of solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

Miller

2017

Water

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Using metaheuristic optimization methods has enabled researchers to reduce the electricity consumption cost of small water distribution systems (WDSs). However, dealing with complicated WDSs and reducing their environmental footprint remains a challenge. In this study a multi-objective version of Pollution Emission Pump Station Optimization tool (PEPSO) is introduced that can reduce the electricity cost and pollution emissions (associated with the energy consumption) of pumps of WDSs. PEPSO includes a user-friendly graphical interface and a customized version of the non-dominated sorting genetic algorithm. A measure that is called "Undesirability Index" (UI) is defined to assist the search for a promising optimization path. The UI also ensures that the final results are desirable and practical. The various features of PEPSO are tested under six scenarios for optimizing the WDS of Monroe City, MI, and Richmond, UK. The test results indicate that in a reasonable amount of time, PEPSO can optimize and provide practical results for both WDSs.

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Section: Literature Reviewmentioning

confidence: 99%

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

Miller

2017

Water

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“…The optimization algorithm chosen for the optimization in this paper is a single objective genetic algorithm (GA). Although the new toolkit EPANET2-ETTAR could be linked to any optimization algorithm that requires an external hydraulic software to simulate the solutions, GAs have been selected as they have been extensively applied to water distribution system problems (Goldberg and Kuo, 1987;Savic et al 1997;Kazantzis et al 2002;Sadatiyan Abkenar et al 2014). Here it is important to note that, as with many other evolutionary algorithms, GAs require a set of parameters (population size, Pop.Size, maximum number of iterations, No.Gen, probability of crossover, Pc, and probability of mutation, Pm) and the specification of the type of selection, crossover and mutation operators.…”

Section: Case Studymentioning

confidence: 99%

“…A large part of the research has studied the minimization of operational costs (e.g. Ormsbee and Lansey, 1994;Mackle et al 1995;Kazantzis et al 2002;van Zyl et al 2004;López-Ibáñez et al 2008;Behandisha andWu, 2014, Ibarra andArnal 2014) and, more recently, the reduction of greenhouse gas emissions in order to reduce the environmental impact caused by the energy production (Wu et al 2012a(Wu et al , 2012bSadatiyan Abkenar et al 2014;Stokes et al 2014). The optimization of pump operation has also been studied in conjunction with water quality (Mala-Jetmarova et al 2015) and network leakage (Price and Ostfeld, 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Pump Operation Using Rule-Based Controls in EPANET2: New ETTAR Toolkit and Correction of Energy Computation

Marchi

Simpson

Lambert

2016

J. Water Resour. Plann. Manage.

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16The optimization of pump operations has the potential to reduce operational costs, while still has been tested on a case study, in which a genetic algorithm has been used to optimize different 26 types of controls. Results show that it is possible to find more cost-effective solutions compared 27 to simple controls and, although with longer computational times, let the algorithm create the 28 entire pump control rules. The robustness of the optimized controls found has also been tested.

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“…In GA, each solution is described as a group of chromosomes which contain a string of genes that corresponds to the controls of pressure for a pump during running period [4]. The purpose of pump optimization is to reduce efficiency deviation.…”

Section: The Optimization Principle Of Hydraulic Modelmentioning

confidence: 99%

Application of hydraulic model in optimal operation of water supply pumping station

Fu¹,

Wang²,

Yuan³

et al. 2016

Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

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Abstract.Through the J City water supply network hydraulic model, QF pumping station optimization scheduling scheme is designed by genetic algorithm. And analyzing the working days and weekends in different working conditions. After the implementation of the optimal scheduling scheme, the power consumption of the pump station is reduced by 5.21%. The hydraulic model can be used to provide a high efficiency energy saving scheme for the optimal operation of pumping stations.

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Evaluation of genetic algorithms using discrete and continuous methods for pump optimization of water distribution systems

Cited by 26 publications

References 7 publications

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

PEPSO: Reducing Electricity Usage and Associated Pollution Emissions of Water Pumps

Optimization of Pump Operation Using Rule-Based Controls in EPANET2: New ETTAR Toolkit and Correction of Energy Computation

Application of hydraulic model in optimal operation of water supply pumping station

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