A statistical model for dew point air cooler based on the multiple polynomial regression approach

Akhlaghi, Yousef Golizadeh; Ma, Xiaoli; Zhao, Xudong; Shittu, Samson; Li, Junming

Abstract

Swift assessment of evaporative cooling systems has become a necessity in practical engineering applications of this advanced technology. This paper bypasses details of the performance process and pioneers in developing a statistical model based on the multiple polynomial regression (MPR) to predict the performance of a dew point cooling (DPC) system. Thousands of numerical and experimental data are explored and the statistical model is produced. The developed statistical model correlates the performance parameters with the key operational parameters, including the flow and geometric characteristics. The selected operational parameters are, intake air conditions, including temperature, relative humidity and flow rate as well as the working air fraction over the intake air, while cooling capacity, coefficient of performance (COP), pressure drop, dew point and wet-bulb effectiveness are selected as performance parameters. The considered geometric characteristics are channel height, channel interval and number of layers in heat and mass exchanger. The model with different polynomial degrees is assessed by R2, MRE and MSE metrics. The 8th degree polynomial model is selected. The maximum relative error of the cooling capacity, coefficient of performance, pressure drop, dew point and wet-bulb effectiveness are 6.1%, 7.54%, 0.07%, 3.54% and 2.53% respectively. Finally, as examples, the model is used to predict the performance of the DPC system in random operating conditions and in a dry climate i.e. Las Vegas. Model developed in this study would enable the swift prediction of the DPC system.

Citation

Akhlaghi, Y. G., Ma, X., Zhao, X., Shittu, S., & Li, J. (2019). A statistical model for dew point air cooler based on the multiple polynomial regression approach. Energy, 181, 868-881. https://doi.org/10.1016/j.energy.2019.05.213

• Journal Article Type: Article
• Acceptance Date: May 29, 2019
• Online Publication Date: Jun 3, 2019
• Publication Date: Aug 15, 2019
• Deposit Date: Sep 3, 2019
• Publicly Available Date: Jun 4, 2020
• Journal: Energy
• Print ISSN: 0360-5442
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 181
• Pages: 868-881
• DOI: https://doi.org/10.1016/j.energy.2019.05.213
• Keywords: General Energy; Pollution; Dew point cooling; Multiple polynomial regression; Operational parameters; Performance parameters; Statistical model
• Public URL: https://hull-repository.worktribe.com/output/1953401
• Contract Date: Sep 3, 2019