A robust decision support leader-follower framework for design of contamination warning system in water distribution network

Khorshidi, Majid Akbarzadeh; Nikoo, Mohammad Reza; Ebrahimi, Elham; Sadegh, Mojtaba

doi:10.1016/j.jclepro.2019.01.010

Cited by 34 publications

(6 citation statements)

References 33 publications

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“…In this regard, water utilities have made substantial efforts to address the contamination events in WDNs with safety and security options such as disinfection, water quality monitoring and warning system, isolation valve control, hydrant flushing, and backflow prevention (Khorshidi et al 2019;Hu et al 2020). However, the WDNs have spatially extensive service coverage and complex configuration with storages and actuators (pumps and valves).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the multi-dimensional resilience of water distribution networks to contamination events

2023

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Recent contamination events in water distribution networks (WDNs) suggest the need for resilience strategies to address the uncertainty of contamination intrusion. Resilience-based decision-making requires a resilience evaluation process. However, the suggestions on resilience options can vary with the definition of the system's functionality considered in the resilience evaluation. Thus, this study characterized resilience and its attributes (i.e., robustness (RS), loss rate (LR), recovery rate (RR), failure duration (FD), and recovery completeness (RC)) in multiple functional dimensions for a WDN to contamination events. The resilience evaluation was performed using a resilience measure based on a time-dependent functionality variation during contamination events. This study considered the functionalities in four dimensions: the contaminated node (CN), compromised demand (CD), biodegradable dissolved organic carbon (BDOC) concentration (BC), and mass consumed (MC). The hydraulic and water quality models were simulated using EPANET-MSX to evaluate the functionality variation under bacterial intrusion events. The results noted that the resilience levels significantly varied with the functional dimensions, relatedness, and contamination event conditions. The results also identified that different profiles of resilience attributes could characterize similar levels of multi-dimensional resilience. The findings suggest insights on incorporating the concept of multi-dimensional resilience in decision-making processes, therefore achieving the goal of improving the overall resilience of the system in diverse aspects of functionality.

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Section: Introductionmentioning

confidence: 99%

Evaluating the multi-dimensional resilience of water distribution networks to contamination events

2023

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“…Sensor optimization investigation in the network started in the 1990s and has continued up to now. The issue based on the number of objective functions is classified as a single objective optimization problem (Al-Zahrani and Moeid 2001;Berry et al 2006Berry et al , 2009Cheifetz et al 2015;Comboul and Ghanem 2013;Hu et al 2017;Kessler et al 1998;Ohar et al 2015;Ostfeld andSalomons 2004, 2005;Propato 2006;Propato and Piller 2006;Schwartz et al 2014;Shastri and Diwekar 2006;Woo et al 2001) and multiple objective optimization problems (Afshar and Miri Khombi 2015;Bazargan-Lari 2014;de Winter et al 2019;Dorini et al 2008;Eliades and Polycarpou 2006;Gueli 2008;He et al 2018;Huang et al 2008;Khorshidi et al 2018Khorshidi et al , 2019McKenna et al 2006;Naserizade et al 2018;Nazempour et al 2018;Ostfeld et al 2008;Ostfeld and Salomons 2008;Preis and Ostfeld 2008;Wu and Walski 2006).…”

Section: Introductionmentioning

confidence: 99%

Selecting the best location of water quality sensors in water distribution networks by considering the importance of nodes and contaminations using NSGA-III (case study: Zahedan water distribution network, Iran)

harif

Azizyan

Darmian

et al. 2023

Environ Sci Pollut Res

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Water quality sensors are one of the most effective ways to minimize the catastrophic consequences of pollution in water distribution networks (WDNs). The main challenge is arranging sensors properly in the network. In this study, the NSGA-III algorithm is developed to improve the optimal locations of sensors by balancing four conflicting objectives. 1. Detection likelihood, 2. Expected detection time, 3. Detection redundancy, and 4. The affected nodes before detection. The proposed procedure is based on chlorine concentration variation between defined upper and lower limits. The upper and lower bounds of chlorine concentration were determined utilizing the Monte Carlo simulator. To deal with the problem of a large size matrix of possible contaminants a heuristic method was utilized for selecting a representative collection of contaminations with the same characteristics and effects. Importance coefficients were introduced to avoid the same importance of contamination events and network nodes. The proposed simulation-optimization approach was tested on the benchmark and real water networks, then the optimal Pareto fronts were computed for each of the two sets of conflicting objectives.Moreover, the sensitivity analysis related to the number of sensors installed in the networks was conducted for the results obtained from different objective functions. According to the sensitivity analysis, the Pareto fronts became more efficient when the number of sensors increased. Also increasing the number of sensors to more than 10 and 15 in the benchmark and real systems, respectively, will provide little additional detection likelihood.

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“…Furthermore, in recent years, the optimal location of sensors in the networks has been extensively investigated. The issue based on the number of objective functions is classified as single objective optimization problems (Kessler et al, 1998, Woo et al, 2001, Al-Zahrani & Moeid., 2001, Ostfeld & Salomons., 2004, Ostfeld & Salomons., 2005, Berry et al, 2006, Berry et al, 2009, Propato., 2006, Propato & Piller., 2008, Shastri & Diwekar., 2006, Comboul & Ghanem., 2013, Schwartz et al, 2014, Cheifetz et al, 2015, Ohar et al, 2015 and multiple objective optimization problems (de Winter et al, 2019, Preis & Ostfeld., 2008, McKenna et al, 2006, Dorini et al, 2008, Eliades & Polycarpou., 2008, Gueli., 2008, Huang et al, 2008, Wu & Walski., 2006, Bazargan-Lari., 2014, Afshar & Miri Khombi., 2015, Rathi & Gupta., 2016, Hu et al, 2017, Tinelli et al, 2017, Nazempour et al, 2018, Naserizade et al, 2018, He et al, 2018, Khorshidi et al, 2019a.…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement Optimization in Dynamic Water Distribution Network by Considering the Importance of Nodes and Uncertain Contamination Entrance

harif

Darmian²,

Azizyan

et al. 2022

Preprint

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Urban infrastructure is heavily reliant on water distribution systems. Contaminants entering the water distribution systems (WDS) are one of the most dangerous events that may occur either deliberately or by accident. Water can become polluted with chemicals or biological agents as a result of water flow. This can cause sickness or even death among people who drink the water. Using an Early Warning Detection System (EWDS) is one of the most effective ways to minimize negative consequences on public health. EWDS are sensors that can reduce the damage due to detecting the contamination. The main challenge is to arrange the sensors in the network in the most efficient way. In this study, the Non-Dominated Sorted Genetic Algorithm-II (NSGA-II), a multi-objective optimization approach, is developed to determine the optimal placement of quality sensors in water distribution networks by balancing four conflicting objectives. 1. Sensor detection likelihood, 2. Sensor expected detection time, 3. Sensor detection redundancy, and 4. The affected population before detection. A contamination matrix with 1000 contaminants event was generated, which represented the total possible combinations of pollutants, then the optimal Pareto fronts are obtained for each two conflicting objectives. The importance coefficients are proposed and applied to minimize the pollution detection time and the number of infected populations based on the contamination risks and the node demands, respectively. Moreover, the sensitivity analysis of the results obtained from different objective functions related to the number of sensors installed in the network was conducted.

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A robust decision support leader-follower framework for design of contamination warning system in water distribution network

Cited by 34 publications

References 33 publications

Evaluating the multi-dimensional resilience of water distribution networks to contamination events

Evaluating the multi-dimensional resilience of water distribution networks to contamination events

Selecting the best location of water quality sensors in water distribution networks by considering the importance of nodes and contaminations using NSGA-III (case study: Zahedan water distribution network, Iran)

Sensor Placement Optimization in Dynamic Water Distribution Network by Considering the Importance of Nodes and Uncertain Contamination Entrance

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