IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i18p11606-d916075.html
   My bibliography  Save this article

Review on the Application of Machine Vision in Defrosting and Decondensation on the Surface of Heat Exchanger

Author

Listed:
  • Bin Yang

    (School of Energy and Security Engineering, Tianjin Chengjiang University, Tianjin 300384, China)

  • Xin Zhu

    (School of Energy and Security Engineering, Tianjin Chengjiang University, Tianjin 300384, China)

  • Minzhang Liu

    (School of Energy and Security Engineering, Tianjin Chengjiang University, Tianjin 300384, China)

  • Zhihan Lv

    (College of Art, Uppsala University, s-75105 Uppsala, Sweden)

Abstract

Under low outdoor temperature and high humidity, frost easily forms on the Heat Exchanger (Exchanger) surface on the outdoor side. The formation and growth of this frost layer will seriously impact the Exchanger’s heat extraction process and the system’s energy efficiency, triggering malfunction in the compressor. To this end, this work first analyzes the formation and growth mechanism of Exchanger surface frosting and condensation. It then summarizes the current research status of Machine Vision (MV) technology in defrosting and decondensation. Further, it previews the follow-up research direction. The experimental findings show that MV technology can automatically observe frost and dew, guaranteeing a real-time understanding of the frost layer. Directly obtaining the frost and dew information from the image can significantly save human resources and improve efficiency.

Suggested Citation

  • Bin Yang & Xin Zhu & Minzhang Liu & Zhihan Lv, 2022. "Review on the Application of Machine Vision in Defrosting and Decondensation on the Surface of Heat Exchanger," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11606-:d:916075
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/18/11606/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/18/11606/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Zhaoyang & Wang, Wei & Sun, Yuying & Wang, Shiquan & Deng, Shiming & Lin, Yao, 2021. "Applying image recognition to frost built-up detection in air source heat pumps," Energy, Elsevier, vol. 233(C).
    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. Yunren Sui & Zengguang Sui & Guangda Liang & Wei Wu, 2023. "Superhydrophobic Microchannel Heat Exchanger for Electric Vehicle Heat Pump Performance Enhancement," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

    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. Chen, Siliang & Chen, Kang & Zhu, Xu & Jin, Xinqiao & Du, Zhimin, 2022. "Deep learning-based image recognition method for on-demand defrosting control to save energy in commercial energy systems," Applied Energy, Elsevier, vol. 324(C).
    2. Ling, Weihao & Wu, Jingtao & Li, Xuan & Ma, Jianjun & Ding, Yu & Li, Bingcheng & Zeng, Min, 2023. "Numerical prediction of frosting growth characteristics of microchannel louvered fin heat exchanger," Energy, Elsevier, vol. 283(C).
    3. Tomas Kropas & Giedrė Streckienė & Juozas Bielskus, 2021. "Experimental Investigation of Frost Formation Influence on an Air Source Heat Pump Evaporator," Energies, MDPI, vol. 14(18), pages 1-15, September.

    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:gam:jsusta:v:14:y:2022:i:18:p:11606-:d:916075. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.