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A statistical modeling approach on the performance prediction of indirect evaporative cooling energy recovery systems

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  • Min, Yunran
  • Chen, Yi
  • Yang, Hongxing

Abstract

Indirect evaporative cooling is well-recognized as a sustainable air-cooling solution to pursue a high quality indoor thermal environment with less energy consumption. In hot and humid areas, the application of an Indirect Evaporative Cooling Energy Recovery System (ERIEC) can cool fresh air to its dew point temperature or lower through water evaporation by using exhaust air from air-conditioned spaces. In this research, a statistical modeling approach is developed to predict the performance of the ERIEC under varying outdoor climates, taking into account possible latent heat transfer from fresh air condensation. Based on training data extracted from numerical simulation, a decision tree model was built to identify the occurrence of condensation through conditional expressions on inlet air temperature and relative humidity. A 2-level factorial design was performed to derive correlations for ERIEC performance indicators under non-condensation and condensation states, respectively. As results, the proposed practical model was validated by experimental data within the deviation of 9.52% on wet-bulb efficiency (ηwb) and 7.69% on enlargement coefficient (ε). A field measurement conducted in Hong Kong shows that the proposed model allows fast and precise prediction on ERIEC performance, with the measured total energy recovery of 5.85 kWh/m2 and the predicted value of 5.40 kWh/m2 for 30 days in cooling season. The model developed in this study can be efficiently integrated into simulation tools for the performance prediction of ERIEC assisted air-conditioning system in the building energy assessment.

Suggested Citation

  • Min, Yunran & Chen, Yi & Yang, Hongxing, 2019. "A statistical modeling approach on the performance prediction of indirect evaporative cooling energy recovery systems," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919315193
    DOI: 10.1016/j.apenergy.2019.113832
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    Citations

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    Cited by:

    1. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    2. Cui, Xin & Yan, Weichao & Liu, Yilin & Zhao, Min & Jin, Liwen, 2020. "Performance analysis of a hollow fiber membrane-based heat and mass exchanger for evaporative cooling," Applied Energy, Elsevier, vol. 271(C).
    3. Yan, Weichao & Meng, Xiangzhao & Cui, Xin & Liu, Yilin & Chen, Qian & Jin, Liwen, 2022. "Evaporative cooling performance prediction and multi-objective optimization for hollow fiber membrane module using response surface methodology," Applied Energy, Elsevier, vol. 325(C).
    4. Hossein Moayedi & Amir Mosavi, 2021. "Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings," Energies, MDPI, vol. 14(5), pages 1-25, March.
    5. Shi, Wenchao & Min, Yunran & Ma, Xiaochen & Chen, Yi & Yang, Hongxing, 2022. "Dynamic performance evaluation of porous indirect evaporative cooling system with intermittent spraying strategies," Applied Energy, Elsevier, vol. 311(C).
    6. Qian Chen & Muhammad Burhan & M Kum Ja & Muhammad Wakil Shahzad & Doskhan Ybyraiymkul & Hongfei Zheng & Xin Cui & Kim Choon Ng, 2022. "Hybrid Indirect Evaporative Cooling-Mechanical Vapor Compression System: A Mini-Review," Energies, MDPI, vol. 15(20), pages 1-17, October.
    7. 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).
    8. Ma, Xiaochen & Shi, Wenchao & Yang, Hongxing, 2022. "Study on water spraying distribution to improve the energy recovery performance of indirect evaporative coolers with nozzle arrangement optimization," Applied Energy, Elsevier, vol. 318(C).
    9. 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).
    10. 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).
    11. Shi, Wenchao & Yang, Hongxing & Ma, Xiaochen & Liu, Xiaohua, 2023. "Performance prediction and optimization of cross-flow indirect evaporative cooler by regression model based on response surface methodology," Energy, Elsevier, vol. 283(C).
    12. Yang, Hongxing & Shi, Wenchao & Chen, Yi & Min, Yunran, 2021. "Research development of indirect evaporative cooling technology: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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