Locating Satellite Booster Disinfectant Stations

Lansey, Kevin; Pasha, Fayzul; Pool, Sheina; Elshorbagy, Walid; Uber, James G.

doi:10.1061/(asce)0733-9496(2007)133:4(372)

Cited by 51 publications

(7 citation statements)

References 10 publications

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“…Therefore, it is important to consider the water quality implications of modifying distribution infrastructure. Previous work considered the placement of chlorine boosters stations (Ayvaz and Kentel 2015;Behzadian et al 2012;Boccelli et al 1998;Lansey et al 2007;Ohar and Ostfeld 2014;Prasad et al 2004;Tryby et al 2002Tryby et al , 1999 and water quality sensors (Berry et al 2006(Berry et al , 2009Hart and Murray 2010) within distribution systems.…”

Section: Introductionmentioning

confidence: 99%

“…There has been extensive research in WDS planning models over the last 30 years using physically-based simulation models (Loucks et al 2005;Mays 2000), numerically-based optimization models (Biegler and Grossmann 2004;Grossmann and Biegler 2004;Liebman 1976;Loucks et al 2005;Revelle et al 2004), and combinations of the two (Goulter 1992;Grayman 2006;Maier et al 2014;Razavi et al 2012a;Shamir 1983). The purpose of these models ranges from the selection of water sources to design and placement of treatment choices for new (Kang and Lansey 2012;Newman et al 2014) and modified (Boccelli et al 1998;Lansey et al 2007;Tryby et al 2002) water systems. Because centralized treatment and distributed delivery is the standard model for design and engineering of drinking water systems (Gleick 2003;Sharma et al 2010), planning models usually evaluate a diverse range of treatment approaches, but simplify or exclude the distribution system effects associated with those alternatives.…”

Section: Introductionmentioning

confidence: 99%

“…Because chlorine addition and sensors do not affect system hydraulics, mathematical simplifications (known as linear superposition) enables the integration of the simulated hydraulic flow behavior into the optimization model. Similar approaches are used when flow behavior does not induce changes in the water quality (Ayvaz and Kentel 2015;Helbling and VanBriesen 2009;Lansey et al 2007;Propato and Uber 2004;Tryby et al 2002). In each example, the optimization model can be run using the output from a single simulation model scenario.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Simulation and Optimization Models for Treatment Plant Placement in Drinking Water Systems

Schwetschenau

VanBriesen

Cohon

2019

J. Water Resour. Plann. Manage.

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Drinking water systems are critical to human health and economic development. System design considers treatment infrastructure to remove contaminants from water supplies and distribution system infrastructure to transport and maintain the quality of treated water to households and businesses. Therefore, treatment and distribution components should be evaluated together when considering system designs and the associated effects on water quality. Planning models are often used to aid decision makers in evaluating drinking water system changes over time. This work proposes a planning modeling approach to determine the optimal treatment and alternative supply infrastructure location and capacity within an existing distribution system. The model consists of four stages: (1) simulating system hydraulic and water quality behavior to set optimization model parameters; (2) selecting potential system configurations through optimization; (3) validating the system hydraulic and water quality behavior through simulation; and (4) selecting network treatment configuration. Results indicate an optimal number of treatment locations that provide the best delivered water quality. For the demonstration network, more than three plants did not provide any improvement in water quality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated Simulation and Optimization Models for Treatment Plant Placement in Drinking Water Systems

Schwetschenau

VanBriesen

Cohon

2019

J. Water Resour. Plann. Manage.

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“…Optimization of the water distribution network started with considering the physical characteristics of the hydraulic compo-nents [6], followed by operational characteristics [7] and combination of these [8]. After water quality concern integrated [9], the problem has been investigated by numerous researches by introducing operation types of booster disinfection facilities [10] and with different solution methods [2,4,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…(2), the nodal concentrations for each consumer node ir and monitoring time tr, C ir,tr (M/L 3 ) is limited between the lower bound, C, and the upper bound, C . The term b i;j ir;tr in the expression of C ir,tr is corresponding to the response coefficient: [12]. By applying this principle, the time varying systems hydraulics and chlorine dosages, were illustrated as a linear system [9].…”

Section: Introductionmentioning

confidence: 99%

Chance Constrained Optimization of Booster Chlorination in Water Distribution Networks

Köker

Altan-Sakarya

2014

CLEAN Soil Air Water

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Quality of municipal water is sustained by addition of disinfectant, generally chlorine, to the water distribution network. Because of health problems, chlorine concentration in the network is limited between maximum and minimum limits. Carcinogenic disinfectant by-products start to occur at high concentrations so it is desired to have minimum amount of chlorine without violating the limit. In addition to the health issues, minimum injection amount is favorable concerning cost. Hence, an optimization problem which covers all of these considerations should be modeled. However, there are uncertain factors as chlorine is reactive and decays both over time and space. Thus, probabilistic approach is necessary to obtain reliable and realistic results from the model. In this study, a linear programming model is developed for the chance constrained optimization of the water distribution network. The objective is to obtain minimum amount of injection mass subjected to maintaining more uniformly distributed chlorine concentrations within the limits, while considering the randomness of chlorine concentration by probability distributions. Network hydraulics and chlorine concentration are computed by the network simulation software, EPANET.

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Optimization for Booster Chlorination

Islam¹,

Rodrı́guez²

2019

Encyclopedia of Water

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Contamination in water distribution network (DN) can be catastrophic if free residual disinfection is not present. To ensure drinking water quality in a DN, ensuring detectable levels of free residual chlorine is a common practice in most municipalities. Higher chlorine doses can be applied in this respect, but this can produce detrimental disinfection by‐products (DBPs) such as trihalomethane and Haloacetic acids, and chlorine‐related taste and odor complaints. Booster chlorination can be a possible solution in this regard where additional chlorine dosages are applied where free residual chlorine concentration is less or zero. However, that requires optimization studies to balance various issues related to over chlorination including higher cost related to installation and operation, higher DBPs, and taste and odor complaints. These optimization studies can not only help us in selecting dosages but also can help us in various other aspects including locating booster stations, deciding number of stations, and pumping scheduling. Unlike other optimization studies, booster chlorination‐related optimization studies also require appropriate variables, kinetics, objective functions, constraints, mathematical formulation, algorithm selection, and proper optimization operation. This article will highlight details of all these steps specific to booster chlorination.

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Locating Satellite Booster Disinfectant Stations

Cited by 51 publications

References 10 publications

Integrated Simulation and Optimization Models for Treatment Plant Placement in Drinking Water Systems

Integrated Simulation and Optimization Models for Treatment Plant Placement in Drinking Water Systems

Chance Constrained Optimization of Booster Chlorination in Water Distribution Networks

Optimization for Booster Chlorination

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