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Thermoeconomic optimization of subcooled and superheated vapor compression refrigeration cycle

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  • Selbaş, Reşat
  • Kızılkan, Önder
  • Şencan, Arzu

Abstract

An exergy-based thermoeconomic optimization application is applied to a subcooled and superheated vapor compression refrigeration system. The advantage of using the exergy method of thermoeconomic optimization is that various elements of the system—i.e., condenser, evaporator, subcooling and superheating heat exchangers—can be optimized on their own. The application consists of determining the optimum heat exchanger areas with the corresponding optimum subcooling and superheating temperatures. A cost function is specified for the optimum conditions. All calculations are made for three refrigerants: R22, R134a, and R407c. Thermodynamic properties of refrigerants are formulated using the Artificial Neural Network methodology.

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  • Selbaş, Reşat & Kızılkan, Önder & Şencan, Arzu, 2006. "Thermoeconomic optimization of subcooled and superheated vapor compression refrigeration cycle," Energy, Elsevier, vol. 31(12), pages 2108-2128.
  • Handle: RePEc:eee:energy:v:31:y:2006:i:12:p:2108-2128
    DOI: 10.1016/j.energy.2005.10.015
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
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    11. Yu, Jianlin & Tian, Gaolei & Xu, Zong, 2009. "Exergy analysis of Joule–Thomson cryogenic refrigeration cycle with an ejector," Energy, Elsevier, vol. 34(11), pages 1864-1869.
    12. Zhao, Lei & Cai, Wenjian & Ding, Xudong & Chang, Weichung, 2013. "Model-based optimization for vapor compression refrigeration cycle," Energy, Elsevier, vol. 55(C), pages 392-402.
    13. Yashar Aryanfar & Mamdouh El Haj Assad & Ali Khosravi & Rahman S M Atiqure & Shubham Sharma & Jorge Luis García Alcaraz & Reza Alayi, 2022. "Energy, exergy and economic analysis of combined solar ORC-VCC power plant [Climate Change Indicators: Greenhouse Gases]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 157-167.
    14. Abdul Mujeebu, Muhammad & Alshamrani, Othman Subhi, 2016. "Prospects of energy conservation and management in buildings – The Saudi Arabian scenario versus global trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1647-1663.
    15. Tomasz Łokietek & Wojciech Tuchowski & Dorota Leciej-Pirczewska & Anna Głowacka, 2022. "Heat Recovery from a Wastewater Treatment Process—Case Study," Energies, MDPI, vol. 16(1), pages 1-15, December.
    16. Sun, Zhili & Wang, Qifan & Xie, Zhiyuan & Liu, Shengchun & Su, Dandan & Cui, Qi, 2019. "Energy and exergy analysis of low GWP refrigerants in cascade refrigeration system," Energy, Elsevier, vol. 170(C), pages 1170-1180.
    17. Janghorban Esfahani, Iman & Yoo, Changkyoo, 2014. "A highly efficient combined multi-effect evaporation-absorption heat pump and vapor-compression refrigeration part 2: Thermoeconomic and flexibility analysis," Energy, Elsevier, vol. 75(C), pages 327-337.

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