Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm

Hasan Alimoradi, Madjid Soltani, Pooriya Shahali, Farshad Moradi Kashkooli, Razieh Larizadeh, Kaamran Raahemifar, Mohammad Adibi and Behzad Ghasemi
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Hasan Alimoradi: Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, Iran
Madjid Soltani: Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, Iran
Pooriya Shahali: Department of Aerospace Engineering, Sharif University of Technology, Tehran 956711155, Iran
Farshad Moradi Kashkooli: Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, Iran
Razieh Larizadeh: Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran 193951999, Iran
Kaamran Raahemifar: College of Information Sciences and Technology (IST) Data Science and Artificial Intelligence Program, Penn State University, Pennsylvania, PA 16801, USA
Mohammad Adibi: Department of Mechanical Engineering, Isfahan University, Isfahan 8174673441, Iran
Behzad Ghasemi: Department of Mechanical Engineering, Shahrekord University, Shahrekord 8818634141, Iran

Energies, 2020, vol. 14, issue 1, 1-19

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.

Keywords: cooling tower; packing compaction; artificial neural network (ANN)-PSO; mathematical correlations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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