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Prediction and Analysis of Dew Point Indirect Evaporative Cooler Performance by Artificial Neural Network Method

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  • Tiezhu Sun

    (Hualan Design & Consulting Group, Nanning 530000, China
    School of Urban Planning and Municipal Engineering, Xi’an Polytechnic University, Xi’an 710043, China)

  • Xiaojun Huang

    (Hualan Design & Consulting Group, Nanning 530000, China)

  • Caihang Liang

    (School of Mechano-Electronic Engineering, Guilin University of Electronic Technology, Guilin 541004, China)

  • Riming Liu

    (Hualan Design & Consulting Group, Nanning 530000, China)

  • Xiang Huang

    (School of Urban Planning and Municipal Engineering, Xi’an Polytechnic University, Xi’an 710043, China)

Abstract

The artificial neural network method has been widely applied to the performance prediction of fillers and evaporative coolers, but its application to the dew point indirect evaporative coolers is rare. To fill this research gap, a novel performance prediction model for dew point indirect evaporative cooler based on back propagation neural network was established using Matlab2018. Simulation based on the test date in the moderately humid region of Yulin City (Shaanxi Province, China) finds that: the root mean square error of the evaporation efficiency of the back propagation model is 3.1367, and the r 2 is 0.9659, which is within the acceptable error range. However, the relative error of individual data (sample 7) is a little bit large, which is close to 10%. In order to improve the accuracy of the back propagation model, an optimized model based on particle swarm optimization was established. The relative error of the optimized model is generally smaller than that of the BP neural network especially for sample 7. It is concluded that the optimized artificial neural network is more suitable for solving the performance prediction problem of dew point indirect evaporative cooling units.

Suggested Citation

  • Tiezhu Sun & Xiaojun Huang & Caihang Liang & Riming Liu & Xiang Huang, 2022. "Prediction and Analysis of Dew Point Indirect Evaporative Cooler Performance by Artificial Neural Network Method," Energies, MDPI, vol. 15(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4673-:d:848022
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    References listed on IDEAS

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    1. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
    2. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2016. "Performance prediction of solid desiccant – Vapor compression hybrid air-conditioning system using artificial neural network," Energy, Elsevier, vol. 103(C), pages 618-629.
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    Cited by:

    1. Lixin Wei & Yu Zhang & Lili Ji & Lin Ye & Xuanchen Zhu & Jin Fu, 2022. "Pressure Drop Prediction of Crude Oil Pipeline Based on PSO-BP Neural Network," Energies, MDPI, vol. 15(16), pages 1-12, August.

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