Calibration of Water Distribution Networks by Considering the Uncertainty of Nodal Pressure

Document Type : Research Paper

Authors

1 Associate Professor, Civil Engineering Department, Azarbaijan Shahid Madani University, Tabriz, Iran.

2 M.Sc., Faculty of Engineering, Shahid Madani University of Azerbaijan, Tabriz, Iran.

Abstract

Given that, observational data have different uncertainties, the calibration of water distribution networks based on a definite value can affect the results of the calibrated model. In this research, the calibration of water distribution networks by considering the uncertainty of nodal pressure has been investigated. For this purpose, first, by generating a series of nodal pressure data using the Monte Carlo method, the Hazen-Williams coefficients of the network have been obtained. Then, by producing a series of Hazen-Williams coefficients, the nodal pressure values have been calculated and the results of the two pressures have been compared. The calibration model was performed using the particle swarm optimization algorithm by linking the EPANET simulator in MATLAB. The proposed method has been implemented on the two-loop sample network and real water distribution network of Sufian city. The results show that the application of node pressure uncertainty in network nodes leads to multiple sets of solutions for Hazen-Williams coefficients of pipes with different variation ranges and standard deviations, which according to the average absolute error of 4 and 3.8 percent between the average nodal pressure in the sample and the real network in the non-deterministic mode with the deterministic mode, the proposed method, can be a more accurate method for calibrating networks. However, it should be noted that there are challenges in the modeling and execution time of calibration models.

Keywords

References

 
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Journal of Water and Wastewater Science and Engineering
Volume 8, Issue 3
October 2023
Pages 13-24

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History

  • Receive Date: 04 July 2022
  • Revise Date: 09 December 2022
  • Accept Date: 16 March 2023

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