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Optimal fresh-air utilization strategy for constant temperature and humidity air-conditioning system based on isocost line

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  • Wang, Cuiling
  • Wang, Baolong
  • Cui, Mengdi
  • Wei, Falin

Abstract

The constant temperature and humidity air-conditioning systems extensively applied in buildings and spaces, such as clean rooms and manufacturing facilities, are considerably energy intensive. To provide thermal comfort, ensure working efficiency, or maintain positive pressures in a workshop, a large amount of fresh air must be supplied indoors. The reasonable use of fresh air can reduce the cost (such as energy, CO2 emission, or money cost) of the air-handling process. An optimal fresh-air utilization strategy was developed to minimize the cost of operation based on considerations of the different costs for different handling processes in all working conditions. Straightforward isocost lines were developed to determine the optimal fresh-air ratio. Using this method, the performances of heating, ventilation, and air-conditioning systems in a workshop were simulated and tested. Results demonstrate that the proposed optimal strategy exhibits superior performance in terms of cost savings. Compared with commonly used strategies, the optimal strategy can reduce the total annual primary energy consumption by 8.3%–9.7%; and the reduction can be as high as 35% in the transition seasons. When the optimal fresh-air utilization strategy is applied in the field, the primary energy consumption is reduced by 6.4%–9.8% on typical days.

Suggested Citation

  • Wang, Cuiling & Wang, Baolong & Cui, Mengdi & Wei, Falin, 2023. "Optimal fresh-air utilization strategy for constant temperature and humidity air-conditioning system based on isocost line," Energy, Elsevier, vol. 263(PD).
  • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222027426
    DOI: 10.1016/j.energy.2022.125856
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    References listed on IDEAS

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    1. Seem, J.E. & House, J.M., 2010. "Development and evaluation of optimization-based air economizer strategies," Applied Energy, Elsevier, vol. 87(3), pages 910-924, March.
    2. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    3. Li, Hongkuan & He, Haiyan & Shan, Jiefei & Cai, Jingjing, 2019. "Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 136-148.
    4. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Adaptive full-range decoupled ventilation strategy and air-conditioning systems for cleanrooms and buildings requiring strict humidity control and their performance evaluation," Energy, Elsevier, vol. 168(C), pages 883-896.
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