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Probabilistic optimal design of cleanroom air-conditioning systems facilitating optimal ventilation control under uncertainties

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  • Zhuang, Chaoqun
  • Wang, Shengwei
  • Shan, Kui

Abstract

Buildings with spaces requiring strict temperature and humidity controls, such as pharmaceutical cleanrooms and semiconductor/microchip factories, have been growing very quickly in terms of total floor area and energy consumption. In such buildings, much of the energy is unnecessarily wasted due to the incoordination of system design and operation/control, especially under “off-design” and ever-changing ambient and load conditions. This paper, therefore, proposes a probabilistic optimal design method for cleanroom air-conditioning systems facilitating optimal ventilation control under uncertainties. To consider the effects of asynchronous loads in different zones/spaces with reduced computation demand, a probabilistic diversity factor method is proposed which is a simplified method to quantify the effects of uncertainties of space load diversities in multiple zones/spaces using diversity factors. The proposed design method is implemented and validated in the design optimization of air-conditioning systems for implementing four different ventilation control strategies considering possible and uncertain off-design conditions. The energy and economic performance as well as service satisfaction of the air-conditioning systems are also evaluated and compared. Results show that the proposed design method can obtain the optimal air-conditioning systems with minimum life-cycle cost and superior satisfaction of service.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:71
    DOI: 10.1016/j.apenergy.2019.113576
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    References listed on IDEAS

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    1. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
    2. 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.
    3. 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.
    4. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    5. 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.
    6. Gang, Wenjie & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability," Applied Energy, Elsevier, vol. 159(C), pages 265-275.
    7. An, Jingjing & Yan, Da & Hong, Tianzhen & Sun, Kaiyu, 2017. "A novel stochastic modeling method to simulate cooling loads in residential districts," Applied Energy, Elsevier, vol. 206(C), pages 134-149.
    8. Daud, Abdel-Karim & Ismail, Mahmoud S., 2012. "Design of isolated hybrid systems minimizing costs and pollutant emissions," Renewable Energy, Elsevier, vol. 44(C), pages 215-224.
    9. Huang, Pei & Huang, Gongsheng & Sun, Yongjun, 2018. "Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements," Applied Energy, Elsevier, vol. 213(C), pages 486-498.
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    Cited by:

    1. Min, Yunran & Chen, Yi & Shi, Wenchao & Yang, Hongxing, 2021. "Applicability of indirect evaporative cooler for energy recovery in hot and humid areas: Comparison with heat recovery wheel," Applied Energy, Elsevier, vol. 287(C).
    2. Jia, Zhiyang & Jin, Xinqiao & Lyu, Yuan & Xue, Qi & Du, Zhimin, 2023. "A robust capacity configuration selection method of multiple-chiller system concerned with the uncertainty of annual hourly load profile," Energy, Elsevier, vol. 282(C).
    3. Zhuang, Chaoqun & Wang, Shengwei, 2020. "Risk-based online robust optimal control of air-conditioning systems for buildings requiring strict humidity control considering measurement uncertainties," Applied Energy, Elsevier, vol. 261(C).
    4. 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).

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