LAPSE:2023.28849
Published Article
LAPSE:2023.28849
Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm
April 12, 2023
Abstract
In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The results reveal that cooling tower efficiency is increased by increasing the air flow rate, water flow rate, and packing compaction.
In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The results reveal that cooling tower efficiency is increased by increasing the air flow rate, water flow rate, and packing compaction.
Record ID
Keywords
artificial neural network (ANN)-PSO, cooling tower, mathematical correlations, packing compaction
Suggested Citation
Alimoradi H, Soltani M, Shahali P, Moradi Kashkooli F, Larizadeh R, Raahemifar K, Adibi M, Ghasemi B. Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm. (2023). LAPSE:2023.28849
Author Affiliations
Alimoradi H: Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, Iran
Soltani M: Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, Iran; Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada; School of Optometry and Visio
Shahali P: Department of Aerospace Engineering, Sharif University of Technology, Tehran 956711155, Iran
Moradi Kashkooli F: Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, Iran [ORCID]
Larizadeh R: Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran 193951999, Iran
Raahemifar K: College of Information Sciences and Technology (IST) Data Science and Artificial Intelligence Program, Penn State University, Pennsylvania, PA 16801, USA; Chemical Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, ON N2
Adibi M: Department of Mechanical Engineering, Isfahan University, Isfahan 8174673441, Iran
Ghasemi B: Department of Mechanical Engineering, Shahrekord University, Shahrekord 8818634141, Iran
Soltani M: Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, Iran; Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada; School of Optometry and Visio
Shahali P: Department of Aerospace Engineering, Sharif University of Technology, Tehran 956711155, Iran
Moradi Kashkooli F: Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, Iran [ORCID]
Larizadeh R: Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran 193951999, Iran
Raahemifar K: College of Information Sciences and Technology (IST) Data Science and Artificial Intelligence Program, Penn State University, Pennsylvania, PA 16801, USA; Chemical Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, ON N2
Adibi M: Department of Mechanical Engineering, Isfahan University, Isfahan 8174673441, Iran
Ghasemi B: Department of Mechanical Engineering, Shahrekord University, Shahrekord 8818634141, Iran
Journal Name
Energies
Volume
14
Issue
1
Article Number
E167
Year
2020
Publication Date
2020-12-30
ISSN
1996-1073
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PII: en14010167, Publication Type: Journal Article
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LAPSE:2023.28849
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https://doi.org/10.3390/en14010167
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Apr 12, 2023
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