IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v295y2024ics0360544224007825.html
   My bibliography  Save this article

Multi-objective optimization of CO2 ejector by combined significant variables recognition, ANN surrogate model and multi-objective genetic algorithm

Author

Listed:
  • Liu, Guangdi
  • Pu, Liang
  • Zhao, Hongxia
  • Chen, Zhuang
  • Li, Guangpeng

Abstract

The ejectors are crucial in enhancing the efficiency of the CO2-based refrigeration cycle. Although the ejector can be optimized by experimental and computational fluid dynamic simulation to improve its performance, these methods are time-consuming and complicated. This research aims to present a fast and systematic approach for optimizing CO2 ejector by combining variance analysis, surrogate model, and non-dominated sorting genetic algorithm. Firstly, the key geometric structures affecting the performance of ejector were determined by variance analysis. Secondly, the influence of key geometric structures and boundary conditions on ejector efficiency and entrainment ratio was analyzed by using computational fluid dynamic simulation, and the database was generated. Next, the artificial neural network surrogate model was established to forecast the ejector performance. Finally, the non-dominated sorting genetic algorithm was involved to optimize the ejector for maximizing ejector efficiency and entrainment ratio. The results show that the efficiencies of the optimized ejectors are above 42%, and the entrainment ratios exceed 0.7. Compared with the basic model, the average efficiencies have increased about 13.20%. When the pressure lift is 0.7 MPa, the entrainment ratios of the optimized ejectors are 0.696 (−20 °C ≤ Te < −15 °C), 0.82(-15 °C ≤ Te < −5 °C), 0.60 (−5 °C ≤ Te < 5 °C) and 0.90 (5 °C ≤ Te < 15 °C), respectively. The above research demonstrates that the success of this approach in addressing time-consuming multi-optimization problems. It offers a fast and systematic approach for the multi-objective optimization of CO2 ejector as a valuable reference for engineering applications.

Suggested Citation

  • Liu, Guangdi & Pu, Liang & Zhao, Hongxia & Chen, Zhuang & Li, Guangpeng, 2024. "Multi-objective optimization of CO2 ejector by combined significant variables recognition, ANN surrogate model and multi-objective genetic algorithm," Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007825
    DOI: 10.1016/j.energy.2024.131010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224007825
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131010?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. Fatong Jia & Dazhang Yang & Jing Xie, 2021. "Numerical Investigation on the Performance of Two-Throat Nozzle Ejectors with Different Mixing Chamber Structural Parameters," Energies, MDPI, vol. 14(21), pages 1-16, October.
    2. Chunnanond, Kanjanapon & Aphornratana, Satha, 2004. "Ejectors: applications in refrigeration technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(2), pages 129-155, April.
    3. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    4. Li, Yafei & Deng, Jianqiang, 2022. "Numerical investigation on the performance of transcritical CO2 two-phase ejector with a novel non-equilibrium CFD model," Energy, Elsevier, vol. 238(PC).
    Full references (including those not matched with items on IDEAS)

    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. Du, Yang & Dai, Yiping, 2018. "Off-design performance analysis of a power-cooling cogeneration system combining a Kalina cycle with an ejector refrigeration cycle," Energy, Elsevier, vol. 161(C), pages 233-250.
    2. Petrovic, Andrija & Jovanovic, Milos Z. & Genic, Srbislav & Bugaric, Ugljesa & Delibasic, Boris, 2018. "Evaluating performances of 1-D models to predict variable area supersonic gas ejector performances," Energy, Elsevier, vol. 163(C), pages 270-289.
    3. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    4. Chong, Daotong & Hu, Mengqi & Chen, Weixiong & Wang, Jinshi & Liu, Jiping & Yan, Junjie, 2014. "Experimental and numerical analysis of supersonic air ejector," Applied Energy, Elsevier, vol. 130(C), pages 679-684.
    5. Khennich, Mohammed & Galanis, Nicolas & Sorin, Mikhail, 2016. "Effects of design conditions and irreversibilities on the dimensions of ejectors in refrigeration systems," Applied Energy, Elsevier, vol. 179(C), pages 1020-1031.
    6. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    7. Meyer, A.J. & Harms, T.M. & Dobson, R.T., 2009. "Steam jet ejector cooling powered by waste or solar heat," Renewable Energy, Elsevier, vol. 34(1), pages 297-306.
    8. Chen, Xiangjie & Worall, Mark & Omer, Siddig & Su, Yuehong & Riffat, Saffa, 2013. "Theoretical studies of a hybrid ejector CO2 compression cooling system for vehicles and preliminary experimental investigations of an ejector cycle," Applied Energy, Elsevier, vol. 102(C), pages 931-942.
    9. Karthick, S.K. & Rao, Srisha M.V. & Jagadeesh, G. & Reddy, K.P.J., 2018. "Experimental parametric studies on the performance and mixing characteristics of a low area ratio rectangular supersonic gaseous ejector by varying the secondary flow rate," Energy, Elsevier, vol. 161(C), pages 832-845.
    10. Ali, Mohamed Kamal Ahmed & Fuming, Peng & Younus, Hussein A. & Abdelkareem, Mohamed A.A. & Essa, F.A. & Elagouz, Ahmed & Xianjun, Hou, 2018. "Fuel economy in gasoline engines using Al2O3/TiO2 nanomaterials as nanolubricant additives," Applied Energy, Elsevier, vol. 211(C), pages 461-478.
    11. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    12. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    13. Anas F A Elbarghthi & Saleh Mohamed & Van Vu Nguyen & Vaclav Dvorak, 2020. "CFD Based Design for Ejector Cooling System Using HFOS (1234ze(E) and 1234yf)," Energies, MDPI, vol. 13(6), pages 1-19, March.
    14. Bolt, Andre & Dincer, Ibrahim & Agelin-Chaab, Martin, 2020. "Energy and exergy analyses of hydrogen production process with aluminum and water chemical reaction," Energy, Elsevier, vol. 205(C).
    15. He, S. & Li, Y. & Wang, R.Z., 2009. "Progress of mathematical modeling on ejectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1760-1780, October.
    16. Prieto, Alejandro & Knaack, Ulrich & Klein, Tillmann & Auer, Thomas, 2017. "25 Years of cooling research in office buildings: Review for the integration of cooling strategies into the building façade (1990–2014)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 89-102.
    17. Yap, Wai Kean & Karri, Vishy, 2011. "ANN virtual sensors for emissions prediction and control," Applied Energy, Elsevier, vol. 88(12), pages 4505-4516.
    18. Li, Yangtao & Khajepour, Amir & Devaud, Cécile & Liu, Kaimin, 2017. "Power and fuel economy optimizations of gasoline engines using hydraulic variable valve actuation system," Applied Energy, Elsevier, vol. 206(C), pages 577-593.
    19. Lamberts, Olivier & Chatelain, Philippe & Bourgeois, Nicolas & Bartosiewicz, Yann, 2018. "The compound-choking theory as an explanation of the entrainment limitation in supersonic ejectors," Energy, Elsevier, vol. 158(C), pages 524-536.
    20. 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.

    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:295:y:2024:i:c:s0360544224007825. 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.