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Implementation of Artificial Intelligence in Modeling and Control of Heat Pipes: A Review

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  • Abdul Ghani Olabi

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK)

  • Salah Haridy

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Benha Faculty of Engineering, Benha University, Benha 13511, Egypt)

  • Enas Taha Sayed

    (Chemical Engineering Department, Faculty of Engineering, Minia University, Minya 61519, Egypt)

  • Muaz Al Radi

    (Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Abdul Hai Alami

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates)

  • Firas Zwayyed

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates)

  • Tareq Salameh

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates)

  • Mohammad Ali Abdelkareem

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Chemical Engineering Department, Faculty of Engineering, Minia University, Minya 61519, Egypt)

Abstract

Heat pipe systems have attracted increasing attention recently for application in various heat transfer-involving systems and processes. One of the obstacles in implementing heat pipes in many applications is their difficult-to-model operation due to the many parameters that affect their performance. A promising alternative to classical modeling that emerges to perform accurate modeling of heat pipe systems is artificial intelligence (AI)-based modeling. This research reviews the applications of AI techniques for the modeling and control of heat pipe systems. This work discusses the AI-based modeling of heat pipes focusing on the influence of chosen input parameters and the utilized prediction models in heat pipe applications. The article also highlights various important aspects related to the application of AI models for modeling heat pipe systems, such as the optimal AI model structure, the models overfitting under small datasets conditions, and the use of dimensionless numbers as inputs to the AI models. Also, the application of hybrid AI algorithms (such as metaheuristic optimization algorithms with artificial neural networks) was reviewed and discussed. Next, intelligent control methods for heat pipe systems are investigated and discussed. Finally, future research directions are included for further improving this technology. It was concluded that AI algorithms and models could predict the performance of heat pipe systems accurately and improve their performance substantially.

Suggested Citation

  • Abdul Ghani Olabi & Salah Haridy & Enas Taha Sayed & Muaz Al Radi & Abdul Hai Alami & Firas Zwayyed & Tareq Salameh & Mohammad Ali Abdelkareem, 2023. "Implementation of Artificial Intelligence in Modeling and Control of Heat Pipes: A Review," Energies, MDPI, vol. 16(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:760-:d:1029779
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    References listed on IDEAS

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    1. Jouhara, H. & Chauhan, A. & Nannou, T. & Almahmoud, S. & Delpech, B. & Wrobel, L.C., 2017. "Heat pipe based systems - Advances and applications," Energy, Elsevier, vol. 128(C), pages 729-754.
    2. David A. Reay, 2015. "Thermal energy storage: the role of the heat pipe in performance enhancement," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 10(2), pages 99-109.
    3. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.
    4. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
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    Cited by:

    1. Hegazy Rezk & Abdul Ghani Olabi & Enas Taha Sayed & Samah Ibrahim Alshathri & Mohammad Ali Abdelkareem, 2023. "Optimized Artificial Intelligent Model to Boost the Efficiency of Saline Wastewater Treatment Based on Hunger Games Search Algorithm and ANFIS," Sustainability, MDPI, vol. 15(5), pages 1-16, March.

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