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

Author

Listed:
  • 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
    Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    School of Optometry and Vision Science, Faculty of Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    Waterloo Institute for Sustainable Energy (WISE), University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • 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
    Chemical Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
    Electrical and Computer Engineering Department, Sultan Qaboos University, Al-Khoud, Muscat 123, Oman)

  • Mohammad Adibi

    (Department of Mechanical Engineering, Isfahan University, Isfahan 8174673441, Iran)

  • Behzad Ghasemi

    (Department of Mechanical Engineering, Shahrekord University, Shahrekord 8818634141, Iran)

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.

Suggested Citation

  • Hasan Alimoradi & Madjid Soltani & Pooriya Shahali & Farshad Moradi Kashkooli & Razieh Larizadeh & Kaamran Raahemifar & Mohammad Adibi & Behzad Ghasemi, 2020. "Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm," Energies, MDPI, vol. 14(1), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:167-:d:472653
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    References listed on IDEAS

    as
    1. Madjid Soltani & Alireza Dehghani-Sanij & Ahmad Sayadnia & Farshad M. Kashkooli & Kobra Gharali & SeyedBijan Mahbaz & Maurice B. Dusseault, 2018. "Investigation of Airflow Patterns in a New Design of Wind Tower with a Wetted Surface," Energies, MDPI, vol. 11(5), pages 1-23, April.
    2. Dehghani-sanij, A.R. & Soltani, M. & Raahemifar, K., 2015. "A new design of wind tower for passive ventilation in buildings to reduce energy consumption in windy regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 182-195.
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