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Modeling the cleaning cycle dynamics for air cooling condensers of thermal power plants: Optimization and global sensitivity analysis
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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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Modeling the cleaning cycle dynamics for air cooling condensers of thermal power plants: Optimization and global sensitivity analysis
),
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.
References(29)
C. Zhang, L. J. Zhong, J. Wang. Decoupling between water use and thermoelectric power generation growth in China. Nat Energy, 2018, 3: 792–799.
H. B. Zhai, E. S. Rubin. Performance and cost of wet and dry cooling systems for pulverized coal power plants with and without carbon capture and storage. Energy Policy, 2010, 38: 5653–5660.
R. Peltier. Improve ACC performance with automated pressure washing. Power, 2011, 155: 18.
T. Pogiatzis, E. M. Ishiyama, W. R. Paterson, et al. Identifying optimal cleaning cycles for heat exchangers subject to fouling and ageing. Appl Energy, 2012, 89: 60–66.
A. L. Diaby, S. J. Miklavcic, S. Bari, et al. Evaluation of crude oil heat exchanger network fouling behavior under aging conditions for scheduled cleaning. Heat Transfer Eng, 2016, 37: 1211–1230.
S. O. Duffuaa, M. O. Budair. Scale removal from heat-exchangers: Using energy utilization as a schedule criterion. Appl Energy, 1994, 47: 77–85.
E. M. Ishiyama, W. R. Paterson, D. I. Wilson. Optimum cleaning cycles for heat transfer equipment undergoing fouling and ageing. Chem Eng Sci, 2011, 66: 604–612.
L. G. da Cruz, E. M. Ishiyama, C. Boxler, et al. Value pricing of surface coatings for mitigating heat exchanger fouling. Food Bioprod Process, 2015, 93: 343–363.
F. Smaïli, D. K. Angadi, C. M. Hatch, et al. Optimization of scheduling of cleaning in heat exchanger networks subject to fouling: Sugar industry case study. Food Bioprod Process, 1999, 77: 159–164.
A. K. Sheikh, S. M. Zubair, M. Younas, et al. A risk based heat exchanger analysis subject to fouling: Part II: Economics of heat exchangers cleaning. Energy, 2000, 25: 445–461.
R. Al Ismaili, M. W. Lee, D. I. Wilson, et al. Optimisation of heat exchanger network cleaning schedules: Incorporating uncertainty in fouling and cleaning model parameters. Comput Chem Eng, 2019, 121: 409–421.
O. M. Magens, E. M. Ishiyama, D. I. Wilson. Quantifying the “implementation gap” for antifouling coatings. Appl Therm Eng, 2016, 99: 683–689.
I. M. Sobol. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul, 2001, 55: 271–280.
A. Sappok, Y. J. Wang, R. Q. Wang, et al. Theoretical and experimental analysis of ash accumulation and mobility in ceramic exhaust particulate filters and potential for improved ash management. SAE Int J Fuels Lubr, 2014, 7: 511–524.
D. Q. Kern, R. E. Seaton. Surface fouling: How to calculate limits. Chem Eng Prog, 1959, 55: 71–73.
E. Nebot, J. F. Casanueva, T. Casanueva, et al. Model for fouling deposition on power plant steam condensers cooled with seawater: Effect of water velocity and tube material. Int J Heat Mass Transfer, 2007, 50: 3351–3358.
H. Müller-Steinhagen, M. R. Malayeri, A. P. Watkinson. Heat exchanger fouling: Mitigation and cleaning strategies. Heat Transfer Eng, 2011, 32: 189–196.
J. Berce, M. Zupančič, M. Može, et al. A review of crystallization fouling in heat exchangers. Processes, 2021, 9: 1356.
R. S. T. Ma, N. Epstein. Optimum cycles for falling rate processes. Can J Chem Eng, 1981, 59: 631–633.
G. F. Jia, R. Q. Wang, M. T. Stacey. Investigation of impact of shoreline alteration on coastal hydrodynamics using dimension reduced surrogate based sensitivity analysis. Adv Water Resour, 2019, 126: 168–175.
M. Li, R. Q. Wang, G. F. Jia. Efficient dimension reduction and surrogate-based sensitivity analysis for expensive models with high-dimensional outputs. Reliab Eng Syst Saf, 2020, 195: 106725.
A. Saltelli, P. Annoni, I. Azzini, et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun, 2010, 181: 259–270.
F. Pianosi, F. Sarrazin, T. Wagener. A Matlab toolbox for global sensitivity analysis. Environ Model Software, 2015, 70: 80–85.
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Acknowledgements
This work was supported by the Science and Technology Development Plan of Jilin Province under No. 20210203110SF.
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