Flexible Booster Chlorination: Design and Operation for Water Distribution Systems under Uncertainty

Basupi, Innocent; Nono, Denis

doi:10.1061/ajrua6.0001013

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…Our simulation considers three sources of uncertainty (Basupi & Nono, 2019; Rico‐Ramirez et al., 2007) for multiperiod simulation. In this case, unknown disturbance can be simulated by hijacking or changing the concentrations at specific nodes or links suddenly.…”

Section: Case Studiesmentioning

confidence: 99%

How Effective is Model Predictive Control in Real‐Time Water Quality Regulation? State‐Space Modeling and Scalable Control

Wang

Taha

Abokifa

2021

Water Resources Research

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Before drinking water leaves water treatment plants, chemical disinfection is typically applied to ensure the microbiological safety of the treated water. Water utilities worldwide rely on chlorine-based disinfectants due to their strong antimicrobial activity and low cost. Excess chlorine is usually applied at the treatment plant to prevent microbial recontamination of the treated drinking water as it moves through the pipes of water distribution networks (WDN).Residual chlorine concentrations are routinely monitored to verify that a sufficient residual is maintained throughout WDN. Maintenance of a detectable residual is also typically mandated by state and federal regulations in many countries. For instance, water utilities in the US are required to preserve detectable chlorine residual throughout their WDNs under the Surface Water Treatment Rule (SWTR) (Haas, 1999), and many states have established even more stringent numerical thresholds on the minimum residual concentration (Roth & Cornwell, 2018).Nevertheless, determining the appropriate chlorine dosage to ensure a sufficient residual, particularly at the far ends of WDNs where the water age is the highest, is rather challenging. Applying large doses of chlorine-based disinfectants at the treatment plant has been associated with multiple issues, including the excessive formation of disinfection byproducts as well as aesthetic issues with water taste and odor (Fisher et al., 2011;Hua et al., 2015). Alternatively, the disinfectant can be injected in smaller doses at multiple locations in the network, a practice commonly known as booster disinfection, to maintain a uniform disinfectant concentration throughout the WDN (Tryby et al., 1999). Most recently, to solve the problem of low disinfectant concentrations at critical dead-end nodes with no need of increasing disinfectant dose at sources or installing additional booster stations, the modulation of nodal outflows in WDN is proposed (Avvedimento et al., 2020). For more context of real-time control of water quality in WDN (see Creaco et al., 2019). Literature ReviewOver the past two decades, many studies have investigated the water quality control problem (WQC) of optimizing the locations and/or dosing schedules of booster disinfection systems.A wide range of optimization-based methods was used to solve the WQC problem, including linear programming (LP), quadratic programming (QP), heuristic algorithms such as genetic algorithm (GA), and

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Section: Case Studiesmentioning

confidence: 99%

How Effective is Model Predictive Control in Real‐Time Water Quality Regulation? State‐Space Modeling and Scalable Control

Wang

Taha

Abokifa

2021

Water Resources Research

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“…The blue boxes showcase quantities that are given ahead of time while the red boxes are real-time, actual quantities that are not furnished for the control algorithm, considering that perfect parameters for the MPC controller is unrealistic. Our approach considers three sources of uncertainty [40], [41] for extended period simulation: (i) unknown disturbance (i.e., the chlorine concentration drops suddenly and unexpectedly due to the reaction with unknown intrusive chemicals-sodium arsenite or contamination events happen), (ii) demand uncertainty, and (iii) parameter uncertainty (i.e., estimated decay coefficients of chlorine in pipes and tanks k P and k TK is not accurate). The details and test results are in Section VI.…”

Section: Real-time Implementationmentioning

confidence: 99%

“…Our simulation considers three sources of uncertainty [40], [41] for multi-period simulation. In this case, unknown disturbance can be simulated by hijacking or changing the concentrations at specific nodes or links suddenly.…”

Section: A Three-node Networkmentioning

confidence: 99%

How Effective is Model Predictive Control in Real-Time Water Quality Regulation? State-Space Modeling and Scalable Control

Wang,

Taha,

Abokifa

2020

Preprint

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Real-time water quality control (WQC) in water distribution networks (WDN), the problem of regulating disinfectant levels, is challenging due to lack of (i) a proper control-oriented modeling considering complicated components (junctions, reservoirs, tanks, pipes, pumps, and valves) for water quality modeling and (ii) a corresponding scalable control algorithm that performs realtime water quality regulation. In this paper, we solve the WQC problem by (a) proposing a novel state-space representation of the WQC problem that provides explicit relationship between inputs (chlorine dosage at booster stations) and states/outputs (chlorine concentrations in the entire network) and (b) designing a highly scalable model predictive control (MPC) algorithm that showcases fast response time and resilience against some sources of uncertainty.

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“…Locating chlorine boosters has been demonstrated using EPANET water-quality simulation (Abokifa et al 2019;Ayvaz and Kentel 2015;Basupi and Nono 2019;Seth et al 2017;Vrachimis et al 2021). Among these studies, Seth et al (2017) and Vrachimis et al (2021) focused on security concerns and emergency response in addition to secondary chlorine boosters to manage contamination detection issues.…”

Section: Introductionmentioning

confidence: 99%

“…Among these studies, Seth et al (2017) and Vrachimis et al (2021) focused on security concerns and emergency response in addition to secondary chlorine boosters to manage contamination detection issues. Basupi and Nono (2019) accounted for hydraulic uncertainties for the placement of chlorine boosters. Abokifa et al (2019) highlighted the limitation of the EPANET model especially for water-quality analysis of dead-end pipes and used an advectiondispersion-reaction transport model to solve the chlorine booster location problem.…”

Section: Introductionmentioning

confidence: 99%

Exploring Vulnerable Nodes, Impactful Viral Intrusion Sites, and Viral Infection Risk Reductions Offered by Chlorine Boosters in Municipal Drinking Water Networks

Lee

Wilson

Cooksey

et al. 2022

J. Water Resour. Plann. Manage.

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The effects of drinking water system infrastructure on water quality and health following intrusion events have not been extensively studied. This study proposes a coupling of hydraulic and water-quality modeling with quantitative microbial risk assessment (QMRA) to characterize microbial infection risks. Two networks were considered based on their network configuration. We assumed a continuous intrusion of enterovirus under three scenarios. The location of vulnerable and influential nodes in a looped and a branched network were compared, followed by a comparison of chlorine booster placement to reduce infection risks. The most vulnerable nodes in the branched network were generally downstream of the intrusion site, whereas those for the looped network were in the middle of the network due to tank dynamics. Influential injection nodes for the looped network were also in the middle of the network but mostly located at the upstream nodes for the branched network. A single chlorine booster yielded a risk reduction (47.6%) for the branched network, greater than for the looped network (nearly none). Two chlorine boosters reduced the looped network risks more notably (63%). The generalizability of these results to other networks likely depends upon specific network hydraulics and variability in municipal drinking water use. This work will help public water system managers in identifying vulnerable points in their distribution system and optimal locations for risk reduction strategy implementation.

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Flexible Booster Chlorination: Design and Operation for Water Distribution Systems under Uncertainty

Cited by 8 publications

References 29 publications

How Effective is Model Predictive Control in Real‐Time Water Quality Regulation? State‐Space Modeling and Scalable Control

How Effective is Model Predictive Control in Real‐Time Water Quality Regulation? State‐Space Modeling and Scalable Control

How Effective is Model Predictive Control in Real-Time Water Quality Regulation? State-Space Modeling and Scalable Control

Exploring Vulnerable Nodes, Impactful Viral Intrusion Sites, and Viral Infection Risk Reductions Offered by Chlorine Boosters in Municipal Drinking Water Networks

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