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

2017

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Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies.

Section: Formulation Of the Optimization Modelmentioning

confidence: 99%

Section: Description Of the Optimization Algorithmmentioning

confidence: 99%

Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

2017

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“…However, in an attempt to exploit the excellent computational properties of EPANET 2, the line minimization was not optimized in Siew and Tanyimboh (2012a). While the applications to date suggest that the strategy works well in general Siew et al 2014Siew et al , 2016, relatively poor and inconsistent performance was discovered subsequently, under conditions of extremely low pressure (Seyoum 2015). For the networks investigated, this corresponds roughly to a demand satisfaction of less than around 10 %.…”

Section: Qn I Hnmentioning

confidence: 99%

“…Recent results have demonstrated the advantages of retaining nondominated infeasible solutions until the end of the optimization (Siew and Tanyimboh 2012b;Eskandar et al 2012;Saleh and Tanyimboh 2013Siew et al 2014Siew et al , 2016Tanyimboh and Seyoum 2016) as infeasible solutions usually contain useful genetic materials (Herrera et al 1998).…”

Section: Source Head Variationsmentioning

confidence: 99%

Investigation into the Pressure-Driven Extension of the EPANET Hydraulic Simulation Model for Water Distribution Systems

Seyoum

2016

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Several hydraulic modelling approaches have been proposed previously to simulate pressure -deficient operating conditions in water distribution networks more realistically. EPANET-PDX is a pressure-driven extension of the EPANET 2 hydraulic simulation model that has an embedded logistic nodal head-flow function. The pressure-driven analysis algorithm in EPANET-PDX was investigated, to improve its performance under conditions of extremely low pressure. By integrating a line minimization procedure fully in the computational solution of the system of equations, the algorithm's consistency was improved by increasing its computational efficiency under conditions of extremely low pressure. The examples considered demonstrated that the pressure-driven analysis algorithm proposed is robust, computationally efficient, and the line minimization procedure is applied frequently. Overall, the results suggest that the algorithm is reliable. The formulation proposed is significantly faster than the previous model under conditions of extremely low pressure. The hydraulic and water quality modelling functionality of EPANET 2 was preserved. For the operating conditions with satisfactory pressure, where direct comparisons with EPANET 2 were possible, EPANET 2 was consistently faster.

“…Recently, significantly improved solutions to some benchmark optimization problems were achieved using pressure-driven analysis (Siew et al 2014(Siew et al , 2016. However, the models have not been considered in water quality studies concerned with issues such as loss of disinfection residual and intrusion of contaminants under low-pressure conditions (Rathi and Gupta 2015;Rathi et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Integration of Hydraulic and Water Quality Modelling in Distribution Networks: EPANET-PMX

Seyoum

2017

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Simulation models for water distribution networks are used routinely for many purposes. Some examples are planning, design, monitoring and control. However, under conditions of low pressure, the conventional models that employ demand-driven analysis often provide misleading results. On the other hand, almost all the models that employ pressure-driven analysis do not perform dynamic and/or water quality simulations seamlessly. Typically, they exclude key elements such as pumps, control devices and tanks. EPANET-PDX is a pressure-driven extension of the EPANET 2 simulation model that preserved the capabilities of EPANET 2 including water quality modelling. However, it cannot simulate multiple chemical substances at once. The single-species approach to water quality modelling is inefficient and somewhat unrealistic. The reason is that different chemical substances may co-exist in water distribution networks. This article proposes a fully integrated network analysis model (EPANET-PMX) (pressure-dependent multi-species extension) that addresses these weaknesses. The model performs both steady state and dynamic simulations. It is applicable to any network with various combinations of chemical reactions and reaction kinetics. Examples that demonstrate its effectiveness are included.