2018
DOI: 10.1061/(asce)wr.1943-5452.0000878
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Decreasing the Discoloration Risk of Drinking Water Distribution Systems through Optimized Topological Changes and Optimal Flow Velocity Control

Abstract: In this paper, a new mathematical framework is proposed for maximising the selfcleaning capacity (SCC) of drinking water distribution systems by controlling diurnal peak flow velocities in pipes under normal operation. This is achieved through an optimal change of the network connectivity (topology). We propose an efficient algorithm for network analysis of valve closures, which allows enforcing favourable changes in flow velocities for maximising the SCC by determining an optimal set of links to isolate in fo… Show more

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Cited by 25 publications
(25 citation statements)
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References 29 publications
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“…If the networks have m different demand patterns, then m number of vectors u would need to be generated, and the number of random variables would be 24*m. A common assumption from the modelling perspective (e.g. (Abraham, Blokker, and Stoianov 2017;Blokker, Vreeburg, and Van Dijk 2009)) is that the demands from individual households or connections with similar demand type are aggregated and assigned to a node that represents such an area. In this paper we also follow this assumption, and therefore small pipes connecting the households to the distribution network are not modelled.…”
Section: Part I Demand Pattern Sampling and Generation Of Pareto Frontsmentioning
confidence: 99%
See 1 more Smart Citation
“…If the networks have m different demand patterns, then m number of vectors u would need to be generated, and the number of random variables would be 24*m. A common assumption from the modelling perspective (e.g. (Abraham, Blokker, and Stoianov 2017;Blokker, Vreeburg, and Van Dijk 2009)) is that the demands from individual households or connections with similar demand type are aggregated and assigned to a node that represents such an area. In this paper we also follow this assumption, and therefore small pipes connecting the households to the distribution network are not modelled.…”
Section: Part I Demand Pattern Sampling and Generation Of Pareto Frontsmentioning
confidence: 99%
“…More recently, Quintiliani et al (2017) and Quintiliani et al (2019) addressed the same problem using a multi-objective optimization formulation, in which both the water age and the number of operational interventions are minimized. Although Abraham, Blokker, and Stoianov (2017) used valve management to maximize the self-cleaning capacity of the network to decrease the risk of discolouration during the peak hours of demand using a single objective optimization. Other authors have proposed the optimization of valves' configuration by minimizing operational costs.…”
Section: Introductionmentioning
confidence: 99%
“…More recently, Quintiliani et al (2017) addressed the same problem using a multi-99 objective optimisation formulation, in which both the water age and the number of operational interventions are minimised. Abraham et al (2017) used valve management to maximise the self-cleaning capacity of the network to decrease the risk of discoloration during the peak hours of demand using a single objective optimisation. Other authors have proposed the optimisation of valves' configuration by minimising operational costs.…”
Section: Problem Descriptionmentioning
confidence: 99%
“…A common assumption from the modelling perspective (e.g., (Blokker et al 2009;Abraham et al 2017)) is that the demands from individual households or connections with similar demand type are aggregated and assigned to a node that represents such an area. Here this assumption is also followed, and therefore small pipes connecting the households to the distribution network are not modelled.…”
Section: Part I Demand Pattern Samplingmentioning
confidence: 99%
“…In order to tighten the polyhedral relaxations (7) and avoid unnecessarily large flow bounds, we define a tailored maximum allowed velocity for each link. It is known that the placement of control valves can result in velocity changes across network links (Abraham et al 2018). In order to limit the possibility of discarding optimal solutions from the feasible set, we implement the following heuristic.…”
Section: Case Study 3: Bwflnetmentioning
confidence: 99%