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Thermodynamic analysis of the ejector refrigeration cycle using the artificial neural network

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  • Rashidi, M.M.
  • Aghagoli, A.
  • Raoofi, R.

Abstract

This paper describes the results of the ejector refrigeration cycle using R600 as a working fluid. The evaporator, generator and condenser are assumed as heat exchangers that exchange heat with three external fluids. The evaporator heat capacity is fixed at 5 kW. Effects of temperature difference in the heat exchangers (ΔT) and generator pressure (Pg) on the coefficient of performance, generator and condenser heat rates, ejector entrainment ratio and the pump work are investigated. Engineering equation solver (EES) software is used for calculating the refrigerant properties. A computer program has been written in MATLAB environment is using neural network toolbox and genetic algorithm. New formulation obtained from ANN for this cycle is presented for calculating the target values. Accuracy of ANN model in terms of the root absolute fraction of variance (R) and the mean squared error (MSE) are evaluated. Also Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACOR) are used to find the maximum values of cycle performance.

Suggested Citation

  • Rashidi, M.M. & Aghagoli, A. & Raoofi, R., 2017. "Thermodynamic analysis of the ejector refrigeration cycle using the artificial neural network," Energy, Elsevier, vol. 129(C), pages 201-215.
    • Handle: RePEc:eee:energy:v:129:y:2017:i:c:p:201-215
    DOI: 10.1016/j.energy.2017.04.089
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    References listed on IDEAS

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    Cited by:

    1. Abbas Aghagoli & Mikhail Sorin & Mohammed Khennich, 2022. "Exergy Efficiency and COP Improvement of a CO 2 Transcritical Heat Pump System by Replacing an Expansion Valve with a Tesla Turbine," Energies, MDPI, vol. 15(14), pages 1-16, July.
    2. 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.
    3. Mosaffa, A.H. & Farshi, L. Garousi, 2018. "Thermodynamic and economic assessments of a novel CCHP cycle utilizing low-temperature heat sources for domestic applications," Renewable Energy, Elsevier, vol. 120(C), pages 134-150.
    4. Haghparast, Payam & Sorin, Mikhail V. & Nesreddine, Hakim, 2018. "The impact of internal ejector working characteristics and geometry on the performance of a refrigeration cycle," Energy, Elsevier, vol. 162(C), pages 728-743.
    5. Damoon Aghazadeh Dokandari & S. M. S. Mahmoudi & M. Bidi & Ramin Haghighi Khoshkhoo & Marc A. Rosen, 2018. "First and Second Law Analyses of Trans-critical N 2 O Refrigeration Cycle Using an Ejector," Sustainability, MDPI, vol. 10(4), pages 1-14, April.
    6. Ferrari, M.L. & Pascenti, M. & Massardo, A.F., 2018. "Validated ejector model for hybrid system applications," Energy, Elsevier, vol. 162(C), pages 1106-1114.
    7. Sun, Lei & Liu, Tianyuan & Wang, Ding & Huang, Chengming & Xie, Yonghui, 2022. "Deep learning method based on graph neural network for performance prediction of supercritical CO2 power systems," Applied Energy, Elsevier, vol. 324(C).
    8. Shan, Yong & Zhang, Jing-zhou & Ren, Xiao-wen, 2018. "Numerical modeling on pumping performance of piccolo-tube multi-nozzles supersonic ejector in an oil radiator passage," Energy, Elsevier, vol. 158(C), pages 216-227.
    9. Zhang, Chenghu & Lin, Jiyou & Tan, Yufei, 2019. "A theoretical study on a novel combined organic Rankine cycle and ejector heat pump," Energy, Elsevier, vol. 176(C), pages 81-90.

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