Impact of diffusion and dispersion of contaminants in water distribution networks modelling and monitoring

Piazza, Stefania; Blokker, Mirjam; Freni, Gabriele; Puleo, Valeria; Sambito, Mariacrocetta

doi:10.2166/ws.2019.131

Cited by 28 publications

(13 citation statements)

References 17 publications

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“…The analysis of solute dispersion under the action of both longitudinal diffusion and radial dispersion shows that when the flow velocity in the pipeline is low, the radial diffusion process has a great influence on the results. On the contrary, the radial diffusion process has little effect on the results (Aris 1956;Piazza et al 2020;Ozdemir et al 2021).…”

Section: Introductionmentioning

confidence: 94%

Diffusion of point source pollution at the cross joint

Sun

Peng

et al. 2022

Water Supply

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The cross joint is one of the standard connection types for urban water supply pipelines. The pipeline containing cross joints was taken as the research object using NaCl as the tracer. The turbulent mixing characteristics of water and pollutants at the cross joints and pollutant's migration and diffusion were studied and analysed by numerical analysis and experimental measurement. The primary purpose is to check the comprehensive influence of six factors, such as pipe diameter, inlet flow ratio, outlet flow ratio, location of damage point, flow of brine, and density of brine, on the turbulent mixing of water and brine in the pipeline. The coefficient of variation is used as an evaluation index to evaluate the mixing in the pipeline, and the effective mixing length LEML is used to quantitatively quantify the uniform mixing position of brine in the pipeline. The results show that the inlet flow ratio, outlet flow ratio, and pipe diameter significantly affect LEML in the east outlet of the cross joint. Outlet flow ratio and pipe diameter significantly affect LEML in the cross joint's south outlet direction. In addition, the dimensionless relationship equation representing LEML is fitted through dimensional analysis.

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Section: Introductionmentioning

confidence: 94%

Diffusion of point source pollution at the cross joint

Sun

Peng

et al. 2022

Water Supply

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“…This is advantageous from a computational point of view as it does not produce particular significant errors in purely turbulent flow regimes. Nevertheless, diffusive-dispersive processes assume great importance when low velocities and laminar/transition flow regime take part and affect the positioning of the monitoring system, as highlighted in Piazza et al (2020) [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of diffusion and dispersion processes in water distribution networks through the use of the Péclet number threshold

Piazza

Sambito

Freni

2023

IOP Conf. Ser.: Earth Environ. Sci.

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To model the water quality within the distribution networks, simplified models are used (such as EPANET), which use an advective-reactive approach and neglect relevant phenomena, such as diffusion and dispersion, for low values of the Reynolds number. Although valid in the presence of a purely turbulent flow regime, as they do not produce substantial modelling errors, these simplifications are not applicable equally to all distribution networks. Therefore, the present study defines a threshold of the Péclet number, which relates the effectiveness of mass transport by advection with the effectiveness of mass transport by dispersion or diffusion, in order to establish a priori the behaviour of the water distribution network and find the most suitable model to use. The numerical analysis was applied to a real case study (network of Oreto-Stazione) using EPANET advective model and EPANET-DDR (Dynamic-Dispersion-Reaction) model, developed by the authors.

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“…EPANET [1] is the most commonly used software for water distribution network simulations and uses an advective approach which cannot efficiently analyze contaminant dispersion in the networks. Piazza et al [2] conducted experiments where it was shown that dispersive and diffusive processes must be incorporated in the transport model for less turbulent fluid flows to achieve more accurate results than the pure advection model. Also, EPANET assumes complete mixing in all network junctions, which can be valid only in the case of a single outlet or if there is considerable distance between two junctions.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network

Lučin

Grbčić

Čarija

et al. 2021

Sensors

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In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations.

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Impact of diffusion and dispersion of contaminants in water distribution networks modelling and monitoring

Cited by 28 publications

References 17 publications

Diffusion of point source pollution at the cross joint

Diffusion of point source pollution at the cross joint

Analysis of diffusion and dispersion processes in water distribution networks through the use of the Péclet number threshold

Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network

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