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Model-based optimization for vapor compression refrigeration cycle

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  • Zhao, Lei
  • Cai, Wenjian
  • Ding, Xudong
  • Chang, Weichung

Abstract

This paper presents a model-based optimization strategy for vapor compression refrigeration cycle. Through analyzing each component characteristics and interactions within the cycle, the optimization problem is formulated as minimizing the total operating cost of the energy consuming devices subject to the constraints of mechanical limitations, component interactions, environment conditions and cooling load demands. A MGA (modified genetic algorithm) together with a solution strategy for a group of nonlinear equations is proposed to obtain optimal set point under different operating conditions. Simulation studies are conducted to compare the proposed method with traditional on–off control strategy to evaluate its performance. Experiment results of a real practical system are also presented to demonstrate its feasibility.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:55:y:2013:i:c:p:392-402
    DOI: 10.1016/j.energy.2013.02.071
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    Cited by:

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    2. Chen, Yi & Han, Wei & Jin, Hongguang, 2015. "An absorption–compression refrigeration system driven by a mid-temperature heat source for low-temperature applications," Energy, Elsevier, vol. 91(C), pages 215-225.
    3. Yin, Xiaohong & Wang, Xinli & Li, Shaoyuan & Cai, Wenjian, 2016. "Energy-efficiency-oriented cascade control for vapor compression refrigeration cycle systems," Energy, Elsevier, vol. 116(P1), pages 1006-1019.
    4. Wang, Xinli & Cai, Wenjian & Lu, Jiangang & Sun, Youxian & Zhao, Lei, 2015. "Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm," Energy, Elsevier, vol. 82(C), pages 939-948.
    5. Janghorban Esfahani, Iman & Kang, Yong Tae & Yoo, ChangKyoo, 2014. "A high efficient combined multi-effect evaporation–absorption heat pump and vapor-compression refrigeration part 1: Energy and economic modeling and analysis," Energy, Elsevier, vol. 75(C), pages 312-326.
    6. Mario Pérez-Gomariz & Antonio López-Gómez & Fernando Cerdán-Cartagena, 2023. "Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review," Clean Technol., MDPI, vol. 5(1), pages 1-21, January.
    7. Mingzhang Pan & Huan Zhao & Dongwu Liang & Yan Zhu & Youcai Liang & Guangrui Bao, 2020. "A Review of the Cascade Refrigeration System," Energies, MDPI, vol. 13(9), pages 1-26, May.
    8. Edoardo Di Mattia & Agostino Gambarotta & Emanuela Marzi & Mirko Morini & Costanza Saletti, 2022. "Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage," Energies, MDPI, vol. 15(19), pages 1-22, September.
    9. Li, Yinlong & Liu, Guoqiang & Chen, Qi & Yan, Gang, 2023. "Progress of auto-cascade refrigeration systems performance improvement: Composition separation, shift and regulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    10. Sebastian Angermeier & Christian Karcher, 2020. "Model-Based Condenser Fan Speed Optimization of Vapor Compression Systems," Energies, MDPI, vol. 13(22), pages 1-26, November.

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