IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i13p3538-d1694695.html
   My bibliography  Save this article

Energy Efficiency Optimization of Air Conditioning Systems Towards Low-Carbon Cleanrooms: Review and Future Perspectives

Author

Listed:
  • Xinran Zeng

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Chunhui Li

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China
    College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore)

  • Xiaoying Li

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Chennan Mao

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Zhengwei Li

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Zhenhai Li

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

Abstract

The advancement of high-tech industries, notably in semiconductor manufacturing, pharmaceuticals, and precision instrumentation, has imposed stringent requirements on cleanroom environments, where strict control of airborne particulates, microbial presence, temperature, and humidity is essential. However, these controlled environments incur significant energy consumption, with air conditioning systems accounting for 40–60% of total usage due to high air circulation rates, intensive treatment demands, and system resistance. In light of global carbon reduction goals and escalating energy costs, improving the energy efficiency of cleanroom heating, ventilation, and air conditioning (HVAC) systems has become a critical research priority. Recent efforts have focused on optimizing airflow distribution, integrating heat recovery technologies, and adopting low-resistance filtration to reduce energy demand while maintaining stringent environmental standards. Concurrently, artificial intelligence (AI) methods, such as machine learning, deep learning, and adaptive control, are being employed to enable intelligent, energy-efficient system operations. This review systematically examines current energy-saving technologies and strategies in cleanroom HVAC systems, assesses their real-world performance, and highlights emerging trends. The objective is to provide a scientific basis for the green design, operation, and retrofit of cleanrooms, thereby supporting the industry’s transition toward low-carbon, sustainable development.

Suggested Citation

  • Xinran Zeng & Chunhui Li & Xiaoying Li & Chennan Mao & Zhengwei Li & Zhenhai Li, 2025. "Energy Efficiency Optimization of Air Conditioning Systems Towards Low-Carbon Cleanrooms: Review and Future Perspectives," Energies, MDPI, vol. 18(13), pages 1-37, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3538-:d:1694695
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/13/3538/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/13/3538/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mawson, Victoria Jayne & Hughes, Ben Richard, 2021. "Optimisation of HVAC control and manufacturing schedules for the reduction of peak energy demand in the manufacturing sector," Energy, Elsevier, vol. 227(C).
    2. Min-Suk Jo & Jang-Hoon Shin & Won-Jun Kim & Jae-Weon Jeong, 2017. "Energy-Saving Benefits of Adiabatic Humidification in the Air Conditioning Systems of Semiconductor Cleanrooms," Energies, MDPI, vol. 10(11), pages 1-23, November.
    3. Shan, Kui & Wang, Shengwei, 2017. "Energy efficient design and control of cleanroom environment control systems in subtropical regions – A comparative analysis and on-site validation," Applied Energy, Elsevier, vol. 204(C), pages 582-595.
    4. Huang, Bin-Juine & Hou, Tung-Fu & Hsu, Po-Chien & Lin, Tse-Han & Chen, Yan-Tze & Chen, Chi-Wen & Li, Kang & Lee, K.Y., 2016. "Design of direct solar PV driven air conditioner," Renewable Energy, Elsevier, vol. 88(C), pages 95-101.
    5. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
    6. Alireza Afshari & Lars Ekberg & Luboš Forejt & Jinhan Mo & Siamak Rahimi & Jeffrey Siegel & Wenhao Chen & Pawel Wargocki & Sultan Zurami & Jianshun Zhang, 2020. "Electrostatic Precipitators as an Indoor Air Cleaner—A Literature Review," Sustainability, MDPI, vol. 12(21), pages 1-22, October.
    7. Li, Tianyu & Yue, Xiao-Guang & Qin, Meng & Norena-Chavez, Diego, 2024. "Towards Paris Climate Agreement goals: The essential role of green finance and green technology," Energy Economics, Elsevier, vol. 129(C).
    8. Zhao, Wenxuan & Li, Hangxin & Wang, Shengwei, 2024. "A generic design optimization framework for semiconductor cleanroom air-conditioning systems integrating heat recovery and free cooling for enhanced energy performance," Energy, Elsevier, vol. 286(C).
    9. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    10. Kong, Dezhou & Hong, Yu & Yang, Yimin & Gu, Tingyue & Fu, Yude & Ye, Yihang & Xi, Weihao & Zhang, Zhiang, 2025. "A parametric, control-integrated and machine learning-enhanced modeling method of demand-side HVAC systems in industrial buildings: A practical validation study," Applied Energy, Elsevier, vol. 379(C).
    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. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    2. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    3. Guo, Yuxiang & Qu, Shengli & Wang, Chuang & Xing, Ziwen & Duan, Kaiwen, 2024. "Optimal dynamic thermal management for data center via soft actor-critic algorithm with dynamic control interval and combined-value state space," Applied Energy, Elsevier, vol. 373(C).
    4. He, Xianya & Huang, Jingzhi & Liu, Zekun & Lin, Jian & Jing, Rui & Zhao, Yingru, 2023. "Topology optimization of thermally activated building system in high-rise building," Energy, Elsevier, vol. 284(C).
    5. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    6. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Probabilistic optimal design of cleanroom air-conditioning systems facilitating optimal ventilation control under uncertainties," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Blad, C. & Bøgh, S. & Kallesøe, C. & Raftery, Paul, 2023. "A laboratory test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems," Applied Energy, Elsevier, vol. 337(C).
    8. Homod, Raad Z. & Mohammed, Hayder Ibrahim & Abderrahmane, Aissa & Alawi, Omer A. & Khalaf, Osamah Ibrahim & Mahdi, Jasim M. & Guedri, Kamel & Dhaidan, Nabeel S. & Albahri, A.S. & Sadeq, Abdellatif M. , 2023. "Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent," Applied Energy, Elsevier, vol. 351(C).
    9. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    10. Guo, Fangzhou & Ham, Sang woo & Kim, Donghun & Moon, Hyeun Jun, 2025. "Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building," Applied Energy, Elsevier, vol. 377(PA).
    11. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
    12. Wang, Kung-Jeng & Lin, Chiuhsiang Joe & Dagne, Teshome Bekele & Woldegiorgis, Bereket Haile, 2022. "Bilayer stochastic optimization model for smart energy conservation systems," Energy, Elsevier, vol. 247(C).
    13. Qin, Haosen & Meng, Tao & Chen, Kan & Li, Zhengwei, 2024. "A comparative study of DQN and D3QN for HVAC system optimization control," Energy, Elsevier, vol. 307(C).
    14. Lu, Ruyuan & Li, Xin & Chen, Ronghao & Lei, Aimin & Ma, Xiaoming, 2024. "An Alternative Reinforcement Learning (ARL) control strategy for data center air-cooled HVAC systems," Energy, Elsevier, vol. 308(C).
    15. Thomas Bröthaler & Marcus Rennhofer & Daniel Brandl & Thomas Mach & Andreas Heinz & Gusztáv Újvári & Helga C. Lichtenegger & Harald Rennhofer, 2021. "Performance Analysis of a Facade-Integrated Photovoltaic Powered Cooling System," Sustainability, MDPI, vol. 13(8), pages 1-21, April.
    16. Zeyue Sun & Mohsen Eskandari & Chaoran Zheng & Ming Li, 2022. "Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-20, December.
    17. Andreas Nascimento & Diunay Zuliani Mantegazini & Mauro Hugo Mathias & Matthias Reich & Julian David Hunt, 2025. "O&G, Geothermal Systems, and Natural Hydrogen Well Drilling: Market Analysis and Review," Energies, MDPI, vol. 18(7), pages 1-22, March.
    18. Liao, Chenxin & Miyata, Shohei & Qu, Ming & Akashi, Yasunori, 2025. "Year-round operational optimization of HVAC systems using hierarchical deep reinforcement learning for enhancing indoor air quality and reducing energy consumption," Applied Energy, Elsevier, vol. 390(C).
    19. M. Usman Saleem & Mustafa Shakir & M. Rehan Usman & M. Hamza Tahir Bajwa & Noman Shabbir & Payam Shams Ghahfarokhi & Kamran Daniel, 2023. "Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-21, June.
    20. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:13:p:3538-:d:1694695. 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.