Importance of demand modelling in network water quality models: a review

Blokker, Mirjam; Vreeburg, J.H.G.; Buchberger, Steven G.; Dijk, J. C. van

doi:10.5194/dwesd-1-1-2008

Cited by 13 publications

(10 citation statements)

References 31 publications

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“…These results are in line with previous studies in the literature (Blokker et al., 2008; Zhao et al., 2018) and further motivate the investigation carried out in this research, into the extent to which a different modeling of nodal demands may impact on the optimal placement of water quality sensors. Furthermore, since the impact of TDA and BUA on contaminant propagation changes as a function of the characteristics of the injection site, the optimal sensor layout and detection performance are expected to be sensitive to the demand modeling considered.…”

Section: Motivationsupporting

confidence: 91%

Pulsed Demand Modeling for the Optimal Placement of Water Quality Sensors in Water Distribution Networks

Giudicianni

Campisano

Nardo

et al. 2022

Water Resources Research

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Water distribution networks (WDNs) are critical infrastructures, since the availability of clean water affects population's prosperity and safety. Therefore, suitable water quality is a fundamental requirement that must be assured. However, WDNs are inherently vulnerable to both intentional and accidental contaminations due to their large size and number of served users and access points (e.g., hydrants, consumer connections, tanks, reservoir, and leak points) (Qiu et al., 2021). These are some of the reasons why the safety of drinking water has recently been receiving more attention in the context of water safety plans (WSPs) (Avvedimento et al., 2020;Bartram, 2009).An efficient strategy used for monitoring and securing WDNs against contamination is the installation of a water quality sensor system (WQSS) (AWWA, 2005) that may provide early detection of conditions of risk for the population health. However, the assessment of the contamination risk is highly uncertain due to the variety of contaminants and the conditions for their intrusion in the WDN (time, duration, and location). To maximize the WQSS performance, the most suitable locations must be identified for sensor location, by balancing the system detection reliability and economic investment (Murray et al., 2008). Indeed, securing the whole WDN is infeasible because of budget constraints on the number of installable sensors.In the last decades the problem of the optimal water quality sensor placement has been explored in WDNs, and various methodologies have been proposed to define optimality of WQSSs (Chang et al., 2013;Lee and Deininger, 1992;Uber et al., 2004). However, the challenge is still open especially with reference to the reliability of the adopted hydraulic/water quality models and the applicability of the methodologies to real cases. Furthermore, due to the complexity of WDNs and the huge number of potential contamination scenarios, the problem of the optimal WQSS layout is computationally burdensome, especially for large networks. Therefore, continuous efforts have been made to develop increasingly efficient numerical optimization techniques. For example, Zhao et al. (2016) proposed a branch and bound sensor placement algorithm running on repeated randomly sample subsets of the events to speed up the process. Khorshidi et al. (2018) used the information entropy theory to reduce the computational burden of the problem and warranting objective selection of sensor placement. De Winter et al. (2019) introduced two greedy-based algorithms considering multiple objective functions for investigating the influence of sensor imperfection. Other results were provided by Ciaponi et al. (2019), who combined the water network sectorization and the installation of water quality sensors for improving the detection performance and reducing the investment costs. Giudicianni et al. (2020) presented an approach based on a priori clustering of the WDN and on the installation of sensors at the topologically most central nodes of each cluster in the case of scarc...

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Section: Motivationsupporting

confidence: 91%

Pulsed Demand Modeling for the Optimal Placement of Water Quality Sensors in Water Distribution Networks

Giudicianni

Campisano

Nardo

et al. 2022

Water Resources Research

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“…Currently, many software packages such as WaterGEMS and WaterCAD by Bentley systems, EPANET by the US Environmental Protection Agency, (USEPA) provide option for modelling hydraulic behaviour of DWDS. [87][88][89] The water quality model include a hydraulic component and a water quality component. A DWDS is made up of several hydraulic components such as pipe, valve, storage tank, pump and fittings.…”

Section: Rate Coefficientsmentioning

confidence: 99%

Review of chloramine decay models in drinking water system

Hossain

Chow

Cook

et al. 2022

Environ. Sci.: Water Res. Technol.

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“…As a departure point for more detailed models, we model an IWS as a single supply pipe with all of its customer demand concentrated at its end. Blokker et al [ 26 ] showed that aggregating several hundred customers’ demand into bulk nodes in CWS simulations had little effect on the simulated flow and dispersion, for timescales longer than 30 minutes. We extend far beyond their scope of aggregation, following Abu-Madi and Trifunovic [ 27 ], and aggregate all customers into a single demand at the end of a single supply pipe.…”

Section: Simplifying Assumptions and First-order Modelsmentioning

confidence: 99%

Analytical scaling relations to evaluate leakage and intrusion in intermittent water supply systems

2018

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Intermittent water supplies (IWS) deliver piped water to one billion people; this water is often microbially contaminated. Contaminants that accumulate while IWS are depressurized are flushed into customers’ homes when these systems become pressurized. In addition, during the steady-state phase of IWS, contaminants from higher-pressure sources (e.g., sewers) may continue to intrude where pipe pressure is low. To guide the operation and improvement of IWS, this paper proposes an analytic model relating supply pressure, supply duration, leakage, and the volume of intruded, potentially-contaminated, fluids present during flushing and steady-state. The proposed model suggests that increasing the supply duration may improve water quality during the flushing phase, but decrease the subsequent steady-state water quality. As such, regulators and academics should take more care in reporting if water quality samples are taken during flushing or steady-state operational conditions. Pipe leakage increases with increased supply pressure and/or duration. We propose using an equivalent orifice area (EOA) to quantify pipe quality. This provides a more stable metric for regulators and utilities tracking pipe repairs. Finally, we show that the volume of intruded fluid decreases in proportion to reductions in EOA. The proposed relationships are applied to self-reported performance indicators for IWS serving 108 million people described in the IBNET database and in the Benchmarking and Data Book of Water Utilities in India. This application shows that current high-pressure, continuous water supply targets will require extensive EOA reductions. For example, in order to achieve national targets, utilities in India will need to reduce their EOA by a median of at least 90%.

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Importance of demand modelling in network water quality models: a review

Cited by 13 publications

References 31 publications

Pulsed Demand Modeling for the Optimal Placement of Water Quality Sensors in Water Distribution Networks

Pulsed Demand Modeling for the Optimal Placement of Water Quality Sensors in Water Distribution Networks

Review of chloramine decay models in drinking water system

Analytical scaling relations to evaluate leakage and intrusion in intermittent water supply systems

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