IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v129y2017icp201-215.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217306552
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.04.089?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Jiangfeng & Dai, Yiping & Gao, Lin & Ma, Shaolin, 2009. "A new combined cooling, heating and power system driven by solar energy," Renewable Energy, Elsevier, vol. 34(12), pages 2780-2788.
    2. Sözen, Adnan & Ali Akçayol, M., 2004. "Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle," Applied Energy, Elsevier, vol. 79(3), pages 309-325, November.
    3. Bai, Tao & Yan, Gang & Yu, Jianlin, 2015. "Thermodynamics analysis of a modified dual-evaporator CO2 transcritical refrigeration cycle with two-stage ejector," Energy, Elsevier, vol. 84(C), pages 325-335.
    4. Rashidi, M.M. & Galanis, N. & Nazari, F. & Basiri Parsa, A. & Shamekhi, L., 2011. "Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network," Energy, Elsevier, vol. 36(9), pages 5728-5740.
    5. Socha, Krzysztof & Dorigo, Marco, 2008. "Ant colony optimization for continuous domains," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1155-1173, March.
    6. Arslan, Oguz, 2011. "Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34," Energy, Elsevier, vol. 36(5), pages 2528-2534.
    7. Rashidi, M.M. & Ali, M. & Freidoonimehr, N. & Nazari, F., 2013. "Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm," Energy, Elsevier, vol. 55(C), pages 497-510.
    8. Wang, Jiangfeng & Dai, Yiping & Zhang, Taiyong & Ma, Shaolin, 2009. "Parametric analysis for a new combined power and ejector–absorption refrigeration cycle," Energy, Elsevier, vol. 34(10), pages 1587-1593.
    9. Lin, Chen & Cai, Wenjian & Li, Yanzhong & Yan, Jia & Hu, Yu, 2012. "The characteristics of pressure recovery in an adjustable ejector multi-evaporator refrigeration system," Energy, Elsevier, vol. 46(1), pages 148-155.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bodys, Jakub & Smolka, Jacek & Palacz, Michal & Haida, Michal & Banasiak, Krzysztof & Nowak, Andrzej J. & Hafner, Armin, 2016. "Performance of fixed geometry ejectors with a swirl motion installed in a multi-ejector module of a CO2 refrigeration system," Energy, Elsevier, vol. 117(P2), pages 620-631.
    2. Zhang, Ying & Deng, Shuai & Ni, Jiaxin & Zhao, Li & Yang, Xingyang & Li, Minxia, 2017. "A literature research on feasible application of mixed working fluid in flexible distributed energy system," Energy, Elsevier, vol. 137(C), pages 377-390.
    3. Mousapour, Ashkan & Hajipour, Alireza & Rashidi, Mohammad Mehdi & Freidoonimehr, Navid, 2016. "Performance evaluation of an irreversible Miller cycle comparing FTT (finite-time thermodynamics) analysis and ANN (artificial neural network) prediction," Energy, Elsevier, vol. 94(C), pages 100-109.
    4. Li, Xinguo & Zhang, Qilin & Li, Xiajie, 2013. "A Kalina cycle with ejector," Energy, Elsevier, vol. 54(C), pages 212-219.
    5. Bai, Tao & Yu, Jianlin & Yan, Gang, 2016. "Advanced exergy analysis on a modified auto-cascade freezer cycle with an ejector," Energy, Elsevier, vol. 113(C), pages 385-398.
    6. 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.
    7. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    8. Besagni, Giorgio & Mereu, Riccardo & Inzoli, Fabio, 2016. "Ejector refrigeration: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 373-407.
    9. 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.
    10. Gonca, Guven, 2017. "Exergetic and ecological performance analyses of a gas turbine system with two intercoolers and two re-heaters," Energy, Elsevier, vol. 124(C), pages 579-588.
    11. Yan, Gang & Bai, Tao & Yu, Jianlin, 2016. "Thermodynamic analysis on a modified ejector expansion refrigeration cycle with zeotropic mixture (R290/R600a) for freezers," Energy, Elsevier, vol. 95(C), pages 144-154.
    12. Feng, Yongqiang & Zhang, Yaning & Li, Bingxi & Yang, Jinfu & Shi, Yang, 2015. "Sensitivity analysis and thermoeconomic comparison of ORCs (organic Rankine cycles) for low temperature waste heat recovery," Energy, Elsevier, vol. 82(C), pages 664-677.
    13. Chandramouli, R. & Srinivasa Rao, M.S.S. & Ramji, K., 2015. "Parametric and optimization studies of reheat and regenerative Braysson cycle," Energy, Elsevier, vol. 93(P2), pages 2146-2156.
    14. DeLovato, Nicolas & Sundarnath, Kavin & Cvijovic, Lazar & Kota, Krishna & Kuravi, Sarada, 2019. "A review of heat recovery applications for solar and geothermal power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    15. Huster, Wolfgang R. & Schweidtmann, Artur M. & Mitsos, Alexander, 2020. "Globally optimal working fluid mixture composition for geothermal power cycles," Energy, Elsevier, vol. 212(C).
    16. Dabwan, Yousef N. & Pei, Gang & Gao, Guangtao & Li, Jing & Feng, Junsheng, 2019. "Performance analysis of integrated linear fresnel reflector with a conventional cooling, heat, and power tri-generation plant," Renewable Energy, Elsevier, vol. 138(C), pages 639-650.
    17. Du, S. & Wang, R.Z. & Xia, Z.Z., 2015. "Graphical analysis on internal heat recovery of a single stage ammonia–water absorption refrigeration system," Energy, Elsevier, vol. 80(C), pages 687-694.
    18. Kang, Ligai & Yang, Junhong & An, Qingsong & Deng, Shuai & Zhao, Jun & Wang, Hui & Li, Zelin, 2017. "Effects of load following operational strategy on CCHP system with an auxiliary ground source heat pump considering carbon tax and electricity feed in tariff," Applied Energy, Elsevier, vol. 194(C), pages 454-466.
    19. Kerme, Esa Dube & Orfi, Jamel & Fung, Alan S. & Salilih, Elias M. & Khan, Salah Ud-Din & Alshehri, Hassan & Ali, Emad & Alrasheed, Mohammed, 2020. "Energetic and exergetic performance analysis of a solar driven power, desalination and cooling poly-generation system," Energy, Elsevier, vol. 196(C).
    20. Jiang-Jiang, Wang & Chun-Fa, Zhang & You-Yin, Jing, 2010. "Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China," Applied Energy, Elsevier, vol. 87(4), pages 1247-1259, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:129:y:2017:i:c:p:201-215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.