IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p7077-d925927.html
   My bibliography  Save this article

Cooperative Optimization of A Refrigeration System with A Water-Cooled Chiller and Air-Cooled Heat Pump by Coupling BPNN and PSO

Author

Listed:
  • Qinli Deng

    (School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China
    Hainan Institute of Wuhan University of Technology, No.5 Chuangxin Road, Yazhou District, Sanya 572024, China)

  • Liangxin Xu

    (School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China)

  • Tingfang Zhao

    (School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China)

  • Xuexin Hong

    (Wuhan University of Technology Design and Research Institute Co., Ltd., Wuhan 430070, China)

  • Xiaofang Shan

    (School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China
    Hainan Institute of Wuhan University of Technology, No.5 Chuangxin Road, Yazhou District, Sanya 572024, China)

  • Zhigang Ren

    (School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China
    Hainan Institute of Wuhan University of Technology, No.5 Chuangxin Road, Yazhou District, Sanya 572024, China)

Abstract

Aiming at the issues of unreasonable cooperation schemes and inappropriate setting of parameters of the refrigeration system with multi-chiller plants, this paper presents a cooperative optimization method to improve the energy performance of the system composed of water-cooled chillers and air-cooled heat pumps. The cooperative optimization process includes scheme optimization and parameter optimization. To content the dynamic cooling load, the working sequence of air-cooled heat pumps and water-cooled chillers with variable frequency chilled water pumps is first optimized. Based on the optimal scheme, a back-propagation neural network (BPNN) coupled with particle swarm optimization (PSO) is implemented to explore the preferred operating parameters of multiple chiller plants corresponding to the best coefficient of performance (COP). Compared with the performance of the initial operation module, the energy consumption of the water pump and fan decreases by over 50%, and the COP of the refrigeration system is improved by 16% (COP = 3.85) through the scheme operation. After parameter optimization, the total energy consumption is reduced by 21.7%, and COP is increased by 26.5% (COP = 4.20). Therefore, the proposed cooperative optimization method can provide useful operation guidance for the refrigeration system with multi-chiller plants.

Suggested Citation

  • Qinli Deng & Liangxin Xu & Tingfang Zhao & Xuexin Hong & Xiaofang Shan & Zhigang Ren, 2022. "Cooperative Optimization of A Refrigeration System with A Water-Cooled Chiller and Air-Cooled Heat Pump by Coupling BPNN and PSO," Energies, MDPI, vol. 15(19), pages 1-19, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7077-:d:925927
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/7077/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/19/7077/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Zhenjun & Wang, Shengwei, 2011. "Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm," Applied Energy, Elsevier, vol. 88(1), pages 198-211, January.
    2. Zhang, Qunli & Zhang, Lin & Nie, Jinzhe & Li, Yinlong, 2017. "Techno-economic analysis of air source heat pump applied for space heating in northern China," Applied Energy, Elsevier, vol. 207(C), pages 533-542.
    3. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    4. Yu, F.W. & Chan, K.T., 2008. "Optimization of water-cooled chiller system with load-based speed control," Applied Energy, Elsevier, vol. 85(10), pages 931-950, October.
    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. Xiaoqing Wei & Nianping Li & Jinqing Peng & Jianlin Cheng & Jinhua Hu & Meng Wang, 2017. "Modeling and Optimization of a CoolingTower-Assisted Heat Pump System," Energies, MDPI, vol. 10(5), pages 1-18, May.
    2. Huang, Sen & Zuo, Wangda & Sohn, Michael D., 2016. "Amelioration of the cooling load based chiller sequencing control," Applied Energy, Elsevier, vol. 168(C), pages 204-215.
    3. Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
    4. Chen, Qun & Xu, Yun-Chao & Hao, Jun-Hong, 2014. "An optimization method for gas refrigeration cycle based on the combination of both thermodynamics and entransy theory," Applied Energy, Elsevier, vol. 113(C), pages 982-989.
    5. Ma, Keyan & Liu, Mingsheng & Zhang, Jili, 2021. "Online optimization method of cooling water system based on the heat transfer model for cooling tower," Energy, Elsevier, vol. 231(C).
    6. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
    7. Ono, Hitoi & Ohtani, Yuichi & Matsuo, Minoru & Yamaguchi, Toru & Yokoyama, Ryohei, 2021. "Optimal operation of heat source and air conditioning system with thermal storage tank using nonlinear programming," Energy, Elsevier, vol. 222(C).
    8. Wang, Yijun & Jin, Xinqiao & Shi, Wantao & Wang, Jiangqing, 2019. "Online chiller loading strategy based on the near-optimal performance map for energy conservation," Applied Energy, Elsevier, vol. 238(C), pages 1444-1451.
    9. Sun, Shaobo & Shan, Kui & Wang, Shengwei, 2022. "An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties," Applied Energy, Elsevier, vol. 317(C).
    10. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    11. Li, Gang & Du, Yuqing, 2018. "Performance investigation and economic benefits of new control strategies for heat pump-gas fired water heater hybrid system," Applied Energy, Elsevier, vol. 232(C), pages 101-118.
    12. Xuebin Ma & Junfeng Li & Yucheng Ren & Reaihan E & Qiugang Wang & Jie Li & Sihui Huang & Mingguo Ma, 2022. "Performance and Economic Analysis of the Multi-Energy Complementary Heating System under Different Control Strategies in Cold Regions," Energies, MDPI, vol. 15(21), pages 1-17, November.
    13. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.
    14. Li, Sihui & Gong, Guangcai & Peng, Jinqing, 2019. "Dynamic coupling method between air-source heat pumps and buildings in China’s hot-summer/cold-winter zone," Applied Energy, Elsevier, vol. 254(C).
    15. Juan-Carlos Fraile & Julio San-José & Ana González-Alonso, 2014. "A Boiler Room in a 600-Bed Hospital Complex: Study, Analysis, and Implementation of Energy Efficiency Improvements," Energies, MDPI, vol. 7(5), pages 1-22, May.
    16. Zhenying Zhang & Jiaqi Wang & Meiyuan Yang & Kai Gong & Mei Yang, 2022. "Environmental and Economic Analysis of Heating Solutions for Rural Residences in China," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    17. Wang, Chengshan & Jiao, Bingqi & Guo, Li & Tian, Zhe & Niu, Jide & Li, Siwei, 2016. "Robust scheduling of building energy system under uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 366-376.
    18. Tang, Rui & Wang, Shengwei & Shan, Kui & Cheung, Howard, 2018. "Optimal control strategy of central air-conditioning systems of buildings at morning start period for enhanced energy efficiency and peak demand limiting," Energy, Elsevier, vol. 151(C), pages 771-781.
    19. Xia, Lei & Ma, Zhenjun & Kokogiannakis, Georgios & Wang, Shugang & Gong, Xuemei, 2018. "A model-based optimal control strategy for ground source heat pump systems with integrated solar photovoltaic thermal collectors," Applied Energy, Elsevier, vol. 228(C), pages 1399-1412.
    20. Ge, Gaoming & Xiao, Fu & Xu, Xinhua, 2011. "Model-based optimal control of a dedicated outdoor air-chilled ceiling system using liquid desiccant and membrane-based total heat recovery," Applied Energy, Elsevier, vol. 88(11), pages 4180-4190.

    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:15:y:2022:i:19:p:7077-:d:925927. 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.