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Research Article | Open Access

Modeling the cleaning cycle dynamics for air cooling condensers of thermal power plants: Optimization and global sensitivity analysis

Bo Zhao1Ruo-Qian Wang2( )Shengxian Cao1
School of Automation Engineering, Northeast Electric Power University, Changchun 132012, China
Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, Piscataway 08854, USA
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Abstract

The air-cooled condenser (ACC) technology drives the decoupling of China’s water consumption and energy production. However, the optimal cleaning frequency of the ACC system has yet to be thoroughly studied. We develop a theoretical model for the total cost of the dust-fouling energy loss and direct cleaning service costs. This extended model is the first to consider energy loss in the cleaning and production phases with field validation. The cleaning period is optimized to minimize the total cost. Numerical solutions are sought to demonstrate the relationship between the normalized optimized cleaning period and the dimensionless inputs. An empirical fitting equation is developed for convenient use in industrial applications. An innovative variance-based global sensitivity analysis (SA) is performed to estimate the sensitivity of the optimization result to the input parameters. We found that heat resistance (Rf), installed capacity, utilization rate, grid electricity price (Enet), and cleaning cost rate have substantial impacts. The present study has the potential to improve the cleaning service plan of the onsite maintenance, to provide a theoretical framework for the life cycle analysis of the power plant, and to inform the decision-makers of the priority of data collection and sensor network deployment.

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Journal of Intelligent Construction
Article number: 9180023
Cite this article:
Zhao B, Wang R-Q, Cao S. Modeling the cleaning cycle dynamics for air cooling condensers of thermal power plants: Optimization and global sensitivity analysis. Journal of Intelligent Construction, 2023, 1(3): 9180023. https://doi.org/10.26599/JIC.2023.9180023

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Received: 20 July 2023
Revised: 26 August 2023
Accepted: 13 September 2023
Published: 16 October 2023
© The Author(s) 2023. Published by Tsinghua University Press.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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