IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i10p1887-d232110.html
   My bibliography  Save this article

The Optimized Energy Saving of a Refrigerating Chamber

Author

Listed:
  • Whei-Min Lin

    (Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 807, Taiwan)

  • Chung-Yuen Yang

    (Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 807, Taiwan)

  • Ming-Tang Tsai

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

  • Hong-Jey Gow

    (R&D, Kuen-Ling Machinery Refrigerating Co., Ltd., Kaohsiung 82644, Taiwan)

Abstract

This paper proposes a control strategy for the energy saving of refrigerating chambers. Combining binary coding and proteome reorganization, the binary proteome algorithm (BPA) is proposed to solve this problem. The refrigeration system model is firstly established based on the performance data of compressors and temperature measurements of each refrigerating chamber. The objective function is an averaged coefficient of performance ( COP ), which considers the switching loss of the compressors, power consumption of the compressors, and refrigerating capacity of the chambers. The control strategy is defined as an optimization problem with constraints to avoid the ineffective operation of a refrigeration system for improving the COP . BPA is adopted to solve the control strategy for optimizing energy saving. The effectiveness and efficiency of the BPA are demonstrated using a real system, and the results are compared with the original control strategy. Results show that the average power consumption drops from 115.92 kW to 108.82 kW, and the average COP value rises from 1.92 to 2.03. The proposed control strategy is feasible, robust, and more effective in energy-saving problems. Other than energy saving, the proposed control strategy also has the benefits of reducing the evaporator frost formation, which allows the products to avoid chill damage.

Suggested Citation

  • Whei-Min Lin & Chung-Yuen Yang & Ming-Tang Tsai & Hong-Jey Gow, 2019. "The Optimized Energy Saving of a Refrigerating Chamber," Energies, MDPI, vol. 12(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1887-:d:232110
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/10/1887/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/10/1887/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Clemente García Cutillas & Javier Ruiz Ramírez & Manuel Lucas Miralles, 2017. "Optimum Design and Operation of an HVAC Cooling Tower for Energy and Water Conservation," Energies, MDPI, vol. 10(3), pages 1-27, March.
    2. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    3. Powell, Kody M. & Cole, Wesley J. & Ekarika, Udememfon F. & Edgar, Thomas F., 2013. "Optimal chiller loading in a district cooling system with thermal energy storage," Energy, Elsevier, vol. 50(C), pages 445-453.
    4. Huang, Yanjun & Khajepour, Amir & Bagheri, Farshid & Bahrami, Majid, 2016. "Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 184(C), pages 605-618.
    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. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
    2. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    3. Awais Shah & Deqing Huang & Tianpeng Huang & Umar Farid, 2018. "Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems," Energies, MDPI, vol. 11(11), pages 1-18, October.
    4. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    5. Zbigniew Buryn & Anna Kuczuk & Janusz Pospolita & Rafał Smejda & Katarzyna Widera, 2021. "Impact of Weather Conditions on the Operation of Power Unit Cooling Towers 905 MWe," Energies, MDPI, vol. 14(19), pages 1-19, October.
    6. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    7. Niamsuwan, Sathit & Kittisupakorn, Paisan & Suwatthikul, Ajaree, 2015. "Enhancement of energy efficiency in a paint curing oven via CFD approach: Case study in an air-conditioning plant," Applied Energy, Elsevier, vol. 156(C), pages 465-477.
    8. Svetlana Ratner & Yuri Chepurko & Larisa Drobyshecskaya & Anna Petrovskaya, 2018. "Management of Energy Enterprises: Energy-efficiency Approach in Solar Collectors Industry: The Case of Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 237-243.
    9. Yang, Xiaohu & Yu, Jiabang & Guo, Zengxu & Jin, Liwen & He, Ya-Ling, 2019. "Role of porous metal foam on the heat transfer enhancement for a thermal energy storage tube," Applied Energy, Elsevier, vol. 239(C), pages 142-156.
    10. Wang, Wenqing & Kolditz, Olaf & Nagel, Thomas, 2017. "Parallel finite element modelling of multi-physical processes in thermochemical energy storage devices," Applied Energy, Elsevier, vol. 185(P2), pages 1954-1964.
    11. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    12. Barth, Florian & Schüppler, Simon & Menberg, Kathrin & Blum, Philipp, 2023. "Estimating cooling capacities from aerial images using convolutional neural networks," Applied Energy, Elsevier, vol. 349(C).
    13. Yuan, Zhipeng & Liu, Qi & Luo, Baojun & Li, Zhenming & Fu, Jianqin & Chen, Jingwei, 2018. "Thermodynamic analysis of different oil flooded compression enhanced vapor injection cycles," Energy, Elsevier, vol. 154(C), pages 553-560.
    14. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
    15. Zu, Kan & Qin, Menghao & Cui, Shuqing, 2020. "Progress and potential of metal-organic frameworks (MOFs) as novel desiccants for built environment control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    16. Cui, X. & Islam, M.R. & Mohan, B. & Chua, K.J., 2016. "Theoretical analysis of a liquid desiccant based indirect evaporative cooling system," Energy, Elsevier, vol. 95(C), pages 303-312.
    17. Yang, Zili & Zhang, Kaisheng & Hwang, Yunho & Lian, Zhiwei, 2016. "Performance investigation on the ultrasonic atomization liquid desiccant regeneration system," Applied Energy, Elsevier, vol. 171(C), pages 12-25.
    18. Wang, Kai & Peng, Jinqing & Li, Sihui & Li, Houpei & Zou, Bin & Ma, Tao & Ji, Jie, 2024. "Compressor speed control for optimizing energy matching of PV-driven AC systems during the cooling season," Energy, Elsevier, vol. 298(C).
    19. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    20. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.

    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:jeners:v:12:y:2019:i:10:p:1887-:d:232110. 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.