IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5575-d877485.html
   My bibliography  Save this article

Evolutionary Multi-Objective Optimization Applied to Industrial Refrigeration Systems for Energy Efficiency

Author

Listed:
  • Nadia Nedjah

    (Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 20550-900, Brazil
    These authors contributed equally to this work.)

  • Luiza de Macedo Mourelle

    (Department of Systems Engineering and Computation, State University of Rio de Janeiro, Rio de Janeiro 20550-900, Brazil
    These authors contributed equally to this work.)

  • Marcelo Silveira Dantas Lizarazu

    (Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 20550-900, Brazil
    These authors contributed equally to this work.)

Abstract

Refrigeration systems based on cooling towers and chillers are widely used equipment in industrial buildings, such as shopping centers, gas and oil refineries and power plants, among many others. Cooling towers are used to recover the heat rejected by the refrigeration system. In this work, the refrigeration is composed of cooling towers dotted with ventilators and compression chillers. The growing environmental concerns and the current scenario of scarce water and energy resources have lead to the adoption of actions to obtain the maximum energy efficiency in such refrigeration equipment. This backs up the application of computational intelligence to optimize the operating conditions of the involved equipment and cooling processes. In this context, we utilize multi-objective optimization algorithms to determine the optimal operational setpoints of the cooling system regarding the cooling towers, its fans and the included chillers. We use evolutionary multi-objective optimization to provide the best trade-offs between two conflicting objectives: maximization of the effectiveness of the cooling towers and minimization of the overall power requirement of the refrigeration system. The optimization process respects the constraints to guarantee the correct and safe operation of the equipment when the evolved solution is implemented. In this work, we apply three evolutionary multi-objective algorithms: Non-dominated Sorting Genetic Algorithm (NSGA-II), Micro-Genetic Algorithm (Micro-GA) and Strength Pareto Evolutionary Algorithm (SPEA2). The results obtained are analyzed under different scenarios and models of the cooling system’s equipment, allowing for the selection of the best algorithm and best equipment’s model to achieve energy efficiency of the studied refrigeration system.

Suggested Citation

  • Nadia Nedjah & Luiza de Macedo Mourelle & Marcelo Silveira Dantas Lizarazu, 2022. "Evolutionary Multi-Objective Optimization Applied to Industrial Refrigeration Systems for Energy Efficiency," Energies, MDPI, vol. 15(15), pages 1-27, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5575-:d:877485
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5575/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/15/5575/
    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.
    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. Carlos D. Zuluaga-Ríos & Alejandro Villa-Jaramillo & Sergio D. Saldarriaga-Zuluaga, 2022. "Evaluation of Distributed Generation and Electric Vehicles Hosting Capacity in Islanded DC Grids Considering EV Uncertainty," Energies, MDPI, vol. 15(20), pages 1-17, October.
    2. Nadia Nedjah & Luiza de Macedo Mourelle & Marcelo Silveira Dantas Lizarazu, 2022. "Swarm Intelligence-Based Multi-Objective Optimization Applied to Industrial Cooling Towers for Energy Efficiency," Sustainability, MDPI, vol. 14(19), pages 1-43, September.
    3. Grzegorz Dudek & Paweł Piotrowski & Dariusz Baczyński, 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications," Energies, MDPI, vol. 16(7), pages 1-11, March.

    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. Abou-Ziyan, Hosny Z. & Alajmi, Ali F., 2014. "Effect of load-sharing operation strategy on the aggregate performance of existed multiple-chiller systems," Applied Energy, Elsevier, vol. 135(C), pages 329-338.
    2. 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.
    3. Zhang, Ning & Yin, Shao-You & Li, Min, 2018. "Model-based optimization for a heat pump driven and hollow fiber membrane hybrid two-stage liquid desiccant air dehumidification system," Applied Energy, Elsevier, vol. 228(C), pages 12-20.
    4. Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2023. "A reinforcement learning-enabled iterative learning control strategy of air-conditioning systems for building energy saving by shortening the morning start period," Applied Energy, Elsevier, vol. 334(C).
    5. Chen, Qun & Wang, Yi-Fei & Xu, Yun-Chao, 2015. "A thermal resistance-based method for the optimal design of central variable water/air volume chiller systems," Applied Energy, Elsevier, vol. 139(C), pages 119-130.
    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. 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.
    8. Gao, Dian-ce & Wang, Shengwei & Shan, Kui & Yan, Chengchu, 2016. "A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems," Applied Energy, Elsevier, vol. 164(C), pages 1028-1038.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
    15. Wang, Xinli & Cai, Wenjian & Yin, Xiaohong, 2017. "A global optimized operation strategy for energy savings in liquid desiccant air conditioning using self-adaptive differential evolutionary algorithm," Applied Energy, Elsevier, vol. 187(C), pages 410-423.
    16. 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.
    17. Cheng, Qi & Wang, Shengwei & Yan, Chengchu & Xiao, Fu, 2017. "Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings," Applied Energy, Elsevier, vol. 185(P2), pages 1613-1624.
    18. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    19. Mu, Baojie & Li, Yaoyu & House, John M. & Salsbury, Timothy I., 2017. "Real-time optimization of a chilled water plant with parallel chillers based on extremum seeking control," Applied Energy, Elsevier, vol. 208(C), pages 766-781.
    20. 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).

    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:15:p:5575-:d:877485. 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.