LAPSE:2023.33373
Published Article
LAPSE:2023.33373
Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems
Hafiz M. Asfahan, Uzair Sajjad, Muhammad Sultan, Imtiyaz Hussain, Khalid Hamid, Mubasher Ali, Chi-Chuan Wang, Redmond R. Shamshiri, Muhammad Usman Khan
April 21, 2023
The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the prediction efficiency. The deep learning algorithm was then optimized to increase the learning rate and predictive accuracy with respect to experimental data by tuning the hyperparameters, such as by manipulating the activation functions, the number of hidden layers, and the neurons in each layer by incorporating optimizers, including Adam and RMsprop. The results confirmed the applicability of the method with an overall value of R2 = 0.987 between the input data and ground-truth data, showing that the most competent model could predict the designated output features (Toutdb, wout, and Eoutair). The suggested method is straightforward and was found to be practical in the evaluation of the thermal performance of deployed air conditioning systems under different conditions. The results supported the hypothesis that the proposed deep learning AI algorithm has the potential to explore the feasibility of the three evaporative cooling systems in dynamic ambient conditions for various agricultural and livestock applications.
Keywords
Artificial Intelligence, direct evaporative cooling, evaporative cooling, indirect evaporative cooling, Maisotsenko evaporative cooling
Suggested Citation
Asfahan HM, Sajjad U, Sultan M, Hussain I, Hamid K, Ali M, Wang CC, Shamshiri RR, Khan MU. Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems. (2023). LAPSE:2023.33373
Author Affiliations
Asfahan HM: Department of Agricultural Engineering, Bahauddin Zakariya University, Bosan Road, Multan 60800, Pakistan [ORCID]
Sajjad U: Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan
Sultan M: Department of Agricultural Engineering, Bahauddin Zakariya University, Bosan Road, Multan 60800, Pakistan [ORCID]
Hussain I: Department of Power Mechanical Engineering, National Tsing Hua University, No. 101, Section 2, Guangfu Road, East District, Hsinchu 300, Taiwan
Hamid K: Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan
Ali M: Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China
Wang CC: Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan [ORCID]
Shamshiri RR: Department of Engineering for Crop Production, Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam-Bornim, Germany [ORCID]
Khan MU: Department of Energy Systems Engineering, Faculty of Agricultural Engineering and Technology, University of Agriculture, Faisalabad, Punjab 38040, Pakistan
Journal Name
Energies
Volume
14
Issue
13
First Page
3946
Year
2021
Publication Date
2021-07-01
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14133946, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.33373
This Record
External Link

doi:10.3390/en14133946
Publisher Version
Download
Files
[Download 1v1.pdf] (9.5 MB)
Apr 21, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
154
Version History
[v1] (Original Submission)
Apr 21, 2023
 
Verified by curator on
Apr 21, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.33373
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version