Volume 10, Issue 3 (12-2017)
ijhe 2017, 10(3): 305-316 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Moghaddam A, Mokhtari M, Peirovi R. Optimization of chlorine injection dosage in water distribution networks using GANetXL model
. ijhe 2017; 10 (3) :305-316
URL: http://ijhe.tums.ac.ir/article-1-5902-en.html
URL: http://ijhe.tums.ac.ir/article-1-5902-en.html
1- Department of Water Engineering, College of Agriculture, Urmia University, Urmia, Iran
2- Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
3- Department of Environmental Health Engineering, School of Public Health, Gonabad University of Medical Sciences, Gonabad, Iran, AND Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran ,rpeirovi@yahoo.com
2- Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
3- Department of Environmental Health Engineering, School of Public Health, Gonabad University of Medical Sciences, Gonabad, Iran, AND Department of Environmental Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran ,
Abstract: (4401 Views)
Background and Objective: one of the steps in water treatment to protect microbial quality of water network is disinfection. Chlorine is one of disinfectants. It is necessary to maintain Free Residual Chlorine (FRC) between minimum and maximum throughout the distribution system in accordance to health standards. This study was aimed to optimize Chlorine dosage in water distribution networks using GANetXL model.
Materials and Methods: In this paper for the first time using an add-in called GANetXL optimization that uses a genetic algorithm, the Chlorine injection was optimized in a reference network based on dynamic connection to EPANET2 hydraulic and qualitative analysis in Excel software. The objective function is formulated such that the squared difference between computed chlorine concentrations and the minimum residual concentration at all monitoring nodes at all times is minimum. The decision variables were the optimized injection dose at boosters’ locations.
Results: The injection rate was obtained (minimum: 0, average: 183.87, maximum: 776.57 and total 4412.84 mg/min per a day) at the station as the number of generation was reduced to 200. Critical nodes formed 20% of the total nodes of network.
Conclusion: Based on the results, minimization of Chlorine whilst comply with FRC standard has both health and economical effects. The results can help the water distribution system management in terms of water quality (by maintaining FRC), health promotion and monetary.
Materials and Methods: In this paper for the first time using an add-in called GANetXL optimization that uses a genetic algorithm, the Chlorine injection was optimized in a reference network based on dynamic connection to EPANET2 hydraulic and qualitative analysis in Excel software. The objective function is formulated such that the squared difference between computed chlorine concentrations and the minimum residual concentration at all monitoring nodes at all times is minimum. The decision variables were the optimized injection dose at boosters’ locations.
Results: The injection rate was obtained (minimum: 0, average: 183.87, maximum: 776.57 and total 4412.84 mg/min per a day) at the station as the number of generation was reduced to 200. Critical nodes formed 20% of the total nodes of network.
Conclusion: Based on the results, minimization of Chlorine whilst comply with FRC standard has both health and economical effects. The results can help the water distribution system management in terms of water quality (by maintaining FRC), health promotion and monetary.
Type of Study: Applicable |
Subject:
WATER
Received: 2017/06/11 | Accepted: 2017/10/17 | Published: 2017/12/12
Received: 2017/06/11 | Accepted: 2017/10/17 | Published: 2017/12/12
Send email to the article author
| Rights and Permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
