Chlorine decay prediction in bulk water using the parallel second order model: An analytical solution development

Kohpaei, Ahmad Jabari; Sathasivan, Arumugam

doi:10.1016/j.cej.2011.03.034

Cited by 44 publications

(2 citation statements)

References 18 publications

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“…In the work of Sadiq and Rodriguez (2004) a comprehensive set of this type of models was gathered. The second group consists of dynamic models of first (e.g., Rossman et al, 1994) or second order (e.g., Clark et al, 2002;Kohpaei and Sathasivan, 2011). Both groups are broadly utilised to model the disinfectant decay.…”

Section: Introductionmentioning

confidence: 99%

A biochemical multi-species quality model of a drinking water distribution system for simulation and design

Arminski

Zubowicz

Brdyś

2013

International Journal of Applied Mathematics and Computer Science

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Drinking Water Distribution Systems (DWDSs) play a key role in sustainable development of modern society. They are classified as critical infrastructure systems. This imposes a large set of highly demanding requirements on the DWDS operation and requires dedicated algorithms for on-line monitoring and control to tackle related problems. Requirements on DWDS availability restrict the usability of the real plant in the design phase. Thus, a proper model is crucial. Within this paper a DWDS multi-species quality model for simulation and design is derived. The model is composed of multiple highly inter-connected modules which are introduced to represent chemical and biological species and (above all) their interactions. The chemical part includes the processes of chloramine decay with additional bromine catalysis and reaction with nitrogen compounds. The biological part consists of both heterotrophic and chemo-autotrophic bacteria species. The heterotrophic bacteria are assumed to consume assimilable organic carbon. Autotrophs are ammonia oxidizing bacteria and nitrite oxidizing bacteria species which are responsible for nitrification processes. Moreover, Disinfection By-Products (DBPs) are also considered. Two numerical examples illustrate the derived model's behaviour in normal and disturbance operational states.

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Section: Introductionmentioning

confidence: 99%

A biochemical multi-species quality model of a drinking water distribution system for simulation and design

Arminski

Zubowicz

Brdyś

2013

International Journal of Applied Mathematics and Computer Science

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“…The use of EPANET is common practice for water quality researchers. Kohpaei and Sathasivan (2011) have shown that chlorine decay has an initial fast reaction followed by a slower one. Monteiro et al (2014) show that modeling the first and n th -order decay kinetics for chlorine decay in EPANET has a sufficient level of accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Learning Water Quality Functions from Data for Monitoring and Controlling Water Quality in Networks

Proceedings of the International Conference on Industrial Engineering and Operations Management

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It is envisioned that future Commercial and Institutional (CI) buildings will be installed with a multitude of sensors that will provide water quality measures in real-time. Since water quality does not change instantly, one needs to predict how water quality will change over time and space, and proactively control water quality using chemical additives, or removing contaminants from water, to keep its quality within the regulated safe range for drinking. Therefore, the control systems must first develop water quality functions that can be used in predictive models and that can subsequently be used in a feedback control system to make optimal control decisions on additives, filtering, flushing, pressure and temperature adjustment, and other available control actions. This paper focuses on the development of these predictive models. Water flow including its physical properties (temperature, pressure, etc.), its chemistry (chlorine, pH levels, etc.), and its contaminants are simulated using the easily accessible water network modeling software EPANET. Resultant spatial-temporal water quality data from the simulation model was used to develop macro water quality functions using nonlinear regression and machine learning methods. The computational evaluation using supervised learning shows that these models can predict the water quality well.

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Framework for Optimizing Chlorine and Byproduct Concentrations in Drinking Water Distribution Systems

Fisher¹,

Kastl²,

Shao³

et al. 2018

Journal AWWA

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Chlorine decay prediction in bulk water using the parallel second order model: An analytical solution development

Cited by 44 publications

References 18 publications

A biochemical multi-species quality model of a drinking water distribution system for simulation and design

A biochemical multi-species quality model of a drinking water distribution system for simulation and design

Learning Water Quality Functions from Data for Monitoring and Controlling Water Quality in Networks

Framework for Optimizing Chlorine and Byproduct Concentrations in Drinking Water Distribution Systems

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