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Performance assessment and optimization of water spray strategy for indirect evaporative cooler based on artificial neural network modeling and genetic algorithm

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  • Ma, Xiaochen
  • Shi, Wenchao
  • Lu, Lin
  • Yang, Hongxing

Abstract

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s∙m2) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a comprehensive understanding of the influence of water-spray operating conditions on IEC performance, and provide valuable insights for the design and operation of high-efficient IEC systems in practical applications.

Suggested Citation

  • Ma, Xiaochen & Shi, Wenchao & Lu, Lin & Yang, Hongxing, 2024. "Performance assessment and optimization of water spray strategy for indirect evaporative cooler based on artificial neural network modeling and genetic algorithm," Applied Energy, Elsevier, vol. 368(C).
    • Handle: RePEc:eee:appene:v:368:y:2024:i:c:s0306261924008213
    DOI: 10.1016/j.apenergy.2024.123438
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    References listed on IDEAS

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    1. 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).
    2. Duan, Zhiyin & Zhan, Changhong & Zhang, Xingxing & Mustafa, Mahmud & Zhao, Xudong & Alimohammadisagvand, Behrang & Hasan, Ala, 2012. "Indirect evaporative cooling: Past, present and future potentials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6823-6850.
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