Seamless pressure-deficient water distribution system model

Seyoum

2016

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

Section: Formulation Of the Optimization Modelmentioning

confidence: 99%

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

Siew

Seyoum

2016

“…EPANET-PDX is an extension of EPANET 2. It includes a logistic pressure-driven nodal flow function (Tanyimboh and Templeman 2010) that was incorporated in the system of equations in the global gradient algorithm (Todini and Pilati 1988). The computational solution procedure developed (Seyoum 2015, Seyoum and) includes a line minimization algorithm (Dennis and Schnabel 1996;Press et al 2007) that improves convergence by optimizing the Newton steps in the global gradient algorithm.…”

Section: Integrated Network Analysis Modelmentioning

confidence: 99%

“…It was achieved by embedding the EPANET-PDX pressure-driven hydraulic simulator in EPANET-MSX. Furthermore, to create EPANET-PDX, the logistic pressure-driven nodal flow function (Tanyimboh and Templeman 2010) was embedded in the module for the global gradient algorithm, Bnetsolve ()^, in EPANET 2. The pressure dependency upgrade included a line minimization module, Blinesearch ()^, that performs the line search and backtracking to preserve the computational efficiency of the global gradient algorithm.…”

Section: Appendix: Outline Description Of the Software (Epanet-pmx)mentioning

confidence: 99%

“…The source code of EPANET 2 was modified recently to provide a pressure-driven model called EPANET-PDX (pressuredependent extension) by incorporating a logistic pressuredriven demand function (Tanyimboh and Templeman 2010) in the global gradient algorithm (Todini and Pilati 1988). It can simulate water quality under both normal and low-pressure conditions with only one chemical reaction at a time .…”

Section: Introductionmentioning

confidence: 99%

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

Seyoum

2017

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.

“…The nondominated feasible solutions were evaluated further by calculating the hydraulic reliability and pipe failure tolerance, using a pressure-driven analysis program (PRAAWDS) (Tanyimboh et al 2003;Tanyimboh and Templeman 2010). The program is robust, computationally efficient, and has been tested and used extensively (Czajkowska 2016;Czajkowska and Tanyimboh 2013).…”

Section: Reliability and Failure Tolerance Evaluationmentioning

confidence: 99%

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

2017

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.