Pressure-dependent network water quality modelling

Seyoum

2016

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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

Section: Computational Solution Of the Optimization Problemmentioning

confidence: 99%

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

Siew

Seyoum

2016

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“…Accordingly, the values adopted here were α = 16, β = 34.7, γ = 14.5 and δ = 10.3. Finally, both k b and k were taken as 1.0/day (Carrico and Singer 2009;Helbling and Van Briesen 2009;Seyoum and Tanyimboh 2014). The wall reactivity coefficient k w requires calibration in practice and would require field data to bring about a significant qualitative improvement in the results.…”

Section: Resultsmentioning

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.

“…Pressure-dependent modelling is therefore essential to ensure the simulations are realistic. Pressure-dependent modelling was carried out with the hydraulic models PRAAWDS Tanyimboh and Templeman 2010) and EPANET-PDX (Siew and Tanyimboh 2012a;Seyoum et al 2013;Seyoum and Tanyimboh 2014). It was assumed the pipes can be closed individually; in practice the actual locations of isolation valves would be considered.…”

Section: Hydraulic Reliability Measuresmentioning

confidence: 99%

“…DSR i.e. demand satisfaction ratio is the fraction of the demand that is satisfied with pressure-dependent modelling in EPANET-PDX (pressure-dependent extension) Tanyimboh 2012a, 2012b;Seyoum and Tanyimboh 2014). A desktop computer (with CPU of 3.2 Hz and RAM of 2 GB) was used.…”

Section: Examplementioning

confidence: 99%

Comparison of Surrogate Measures for the Reliability and Redundancy of Water Distribution Systems

Siew

Saleh

et al. 2016

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An investigation into the effectiveness of surrogate measures for the hydraulic reliability and/or redundancy of water distribution systems is presented. The measures considered are statistical flow entropy, resilience index, network resilience and surplus power factor. Looped network designs that are maximally noncommittal to the surrogate reliability measures were considered. In other words, the networks were designed by multi-objective evolutionary optimization free of any influence from the surrogate measures. The designs were then assessed using each surrogate measure and two accurate but computationally intensive measures namely hydraulic reliability and pipe-failure tolerance. The results indicate that by utilising statistical flow entropy, the reliability of the network can be reasonably approximated, with substantial savings in computational effort. The results for the other surrogate measures were often inconsistent. Two networks in the literature were considered. One example involved a range of alternative network topologies. In the other example, based on wholelife cost accounting, alternative design and upgrading schemes for a 20-year design horizon were considered. Pressure-dependent hydraulic modelling was used to simulate pipe failures for the reliability calculations.