Field Verification of an Integrated Hydraulic and Multi-Species Water Quality Model

Alexander, Matthew T.; Boccelli, Dominic L.

doi:10.1061/41203(425)65

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…EPANET-MSX, a recent EPANET extension, enables modeling of complex reactions between multiple chemical and biological species in both the bulk flow and at the pipe wall (Uber, 2009). Examples include the auto-decomposition of chloramines, the formation of disinfection by-products and biological re-growth (Quiroz-Centeno et al 2010, Andrade et al 2010, Alexander and Boccelli, 2010, Rubulis et al 2010.…”

Section: Modeling Wqmentioning

confidence: 99%

Modeling and Field Work to Investigate the Relationship between Age and Quality of Tap Water

Machell

Boxall

2014

J. Water Resour. Plann. Manage.

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Article:Machel, J.M. and Boxall, J.B. (2014) Modelling and field work to investigate the relationship between the age and the quality of drinking water at customer's taps. Journal of Water Resources Planning and Management, 140 (9).1 Modeling and field work to investigate the relationship between the age and the quality of drinking water at customer's taps Abstract: It has been widely theorized that water age may be a useful indicator of the quality of water within drinking water distribution networks. However, there is limited evidence of model simulation results being related to empirical water quality (WQ hereafter) data to substantiate the theory. This paper presents the findings of investigations designed to determine if there was an observable relationship between mean water ages calculated using a WQ simulation model, and measured WQ in two live distribution networks.The age of water in all pipes was calculated using Aquis hydraulic and WQ modeling software. Historic regulatory WQ data was examined to determine if there was a relationship between general WQ and calculated water age within the networks. A more detailed study was then undertaken in one network by translating model locations that were representative of the spread of water age into real world locations.WQ samples were taken intensively from these sites and analyzed for a range of aesthetic, physical, chemical, and bacteriological parameters indicative of general WQ.Some relationship between the calculated mean age of water and the general WQ in the network was demonstrated. Analysis that considered calculated water age and associated WQ along unperturbed flow routes through the network produced a stronger relationship. Given that regulatory WQ within the study networks as reported to the Drinking Water Inspectorate (DWI) was 99.9% compliant, this relationship was deemed noteworthy.

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Section: Modeling Wqmentioning

confidence: 99%

Modeling and Field Work to Investigate the Relationship between Age and Quality of Tap Water

Machell

Boxall

2014

J. Water Resour. Plann. Manage.

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“…10,12 However, their model did not consider the chloramine loss due to microbiological decay and/or wall decay processes. The multispecies chloramine decay model in EPANET-MSX was applied to a real DWDS by Alexander and Boccelli 96 and concluded that it can adequately represent the variation of some species throughout the network, although many other species were characterised poorly. For instance, chloramine was slightly overestimated by no more than 1 mg L −1 at most locations, while free ammonia was poorly estimated at all sampling locations.…”

Section: Environmental Science: Water Research and Technologymentioning

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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“…For the same reason, it is difficult to forecast water quality in chloraminated WDSs using a process-based model. For example, in a study by Alexander and Boccelli (2010) the authors found that a multi-species water quality process model (EPANET-MSX) cannot characterise the changes of the whole range of species throughout chloraminated WDSs. Therefore, in this paper, it is proposed to develop a data-driven ANN model to forecast water quality in a chloraminated WDS -the Goldfield and Agricultural Water System (G&AWS) east of Perth.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks to forecasting water quality in a chloraminated water distribution system

Dandy

Maier

2011

Chan, F., Marinova, D. And Anderssen, R.S. (Eds) MODSIM2011, 19th International Congress on Modelling and Simulation.

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Accurate forecasting of disinfection residuals in a water distribution system (WDS) is essential for optimal control of disinfectant dosing in order to maintain good water quality within the system. The most commonly used disinfectant in the drinking water treatment process throughout the world is chlorine. Previously, artificial neural networks (ANNs) have been used successfully to predict chlorine residuals in chlorinated WDSs. Recently, an increasing number of water utilities are using chloramine as a secondary disinfectant in WDSs. Chloramine has several advantages compared with chlorine including fewer issues with disinfection by-products (DBP) and longer persistence. However, limited research has been conducted into the modelling of disinfection residuals (chlorine and free ammonia) in a chloraminated WDS. In this research, a general regression neural network (GRNN) has been used to forecast chlorine and free ammonia residuals in one section of the Goldfield and Agricultural Water System (G&AWS) east of Perth.In this study, the system under investigation spans from the original chlorine and ammonia dosing location at Mundaring pump stations to Goomalling pump station. Ten water quality variables and six flow variables have been used for the development of both chlorine and free ammonia forecasting models. The data were collected at 10 minutes intervals over a one-year period from February 2009 to January 2010. They have been converted into hourly data for this study. A maximum of 94 lags are used, which result in a total of 1,536 potential inputs for each model. In order to select appropriate inputs for each of the models within a reasonable time, a three-step input selection procedure using both mutual information (MI) and partial mutual information (PMI) has been used, which results in 12 selected inputs for the chlorine forecasting model and 10 selected inputs for the free ammonia forecasting model. A deterministic data splitting method called DUPLEX has been used to divide the data into the training (60%), testing (20%) and validation (20%) sets.The modelling results indicate that ANNs have the ability to forecast chlorine residuals in a chloraminated WDS. This provides a useful tool that can be used to assist in optimal control of disinfectant levels in WDSs. However, further information on both the input variables and the hydraulics of the system is required in order to improve the performance of the chlorine forecasting model. In contrast, the free ammonia forecasting model performed poorly and cannot be used to provide accurate forecasts of free ammonia levels in the system. This is mainly because the free ammonia data collected were estimated based on the difference between the total ammonia nitrogen and the monochloramine nitrogen, and therefore are not accurate. As a result, accurate free ammonia analysers are required in order to obtain precise free ammonia data for the development of an accurate ANN model for the forecasting of the free ammonia levels in a WDS.

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Field Verification of an Integrated Hydraulic and Multi-Species Water Quality Model

Cited by 6 publications

References 2 publications

Modeling and Field Work to Investigate the Relationship between Age and Quality of Tap Water

Modeling and Field Work to Investigate the Relationship between Age and Quality of Tap Water

Review of chloramine decay models in drinking water system

Application of artificial neural networks to forecasting water quality in a chloraminated water distribution system

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