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Application of Artificial Intelligence to Improve the Thermal Energy and Exergy of Nanofluid-Based PV Thermal/Nano-Enhanced Phase Change Material

Author

Listed:
  • Enas Taha Sayed

    (Center for Advanced Materials Research, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Chemical Engineering Department, Faculty of Engineering, Minia University, Minya 61519, Egypt)

  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Abdul Ghani Olabi

    (Center for Advanced Materials Research, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    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)

  • Mohamed R. Gomaa

    (Mechanical Engineering Department, Faculty of Engineering, Al-Hussein Bin Talal University, Ma’an 71111, Jordan)

  • Yahia B. Hassan

    (Electrical Engineering Department, Higher Institute of Engineering, Minia 61519, Egypt)

  • Shek Mohammad Atiqure Rahman

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

  • Sheikh Khaleduzzaman Shah

    (Renewable Energy and Energy Efficiency Group, Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC 3010, Australia)

  • Mohammad Ali Abdelkareem

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

Abstract

Photovoltaic-thermal (PVT) technologies have demonstrated several attractive features, such as higher power and comparative efficiencies. Improving the thermal recovery from the PVT system would further improve the power output and the efficiency of the PVT system. This paper identifies the best operating factors of nanofluid-based PV thermal/nano-enhanced phase change material using artificial intelligence. The target is the maximization of thermal energy and exergy outputs. The suggested approach combines ANFIS modelling and particle swarm optimization (PSO). Four operating factors are taken into consideration: PCM (phase change material) layer thickness, HTF (heat transfer fluid) mass flow rate, MFNPCM (“mass fraction of nanoparticles in PCM”) and MFNfluid (“mass fraction of nanoparticles in nanofluid”). Using a dataset, an “adaptive neuro-fuzzy inference system” (ANFIS) model has been established for simulating the thermal energy and exergy outputs in terms of the mentioned operating factors. Then, using PSO, the best values of PCM thickness, mass flow rate, MFNPCM and MFNfluid are estimated. The proposed model’s accuracy was examined by comparing the results with those obtained by response surface methodology and the experimental dataset.

Suggested Citation

  • Enas Taha Sayed & Hegazy Rezk & Abdul Ghani Olabi & Mohamed R. Gomaa & Yahia B. Hassan & Shek Mohammad Atiqure Rahman & Sheikh Khaleduzzaman Shah & Mohammad Ali Abdelkareem, 2022. "Application of Artificial Intelligence to Improve the Thermal Energy and Exergy of Nanofluid-Based PV Thermal/Nano-Enhanced Phase Change Material," Energies, MDPI, vol. 15(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8494-:d:972130
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    References listed on IDEAS

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    1. Nassef, Ahmed M. & Fathy, Ahmed & Sayed, Enas Taha & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Tanveer, Waqas Hassan & Olabi, A.G., 2019. "Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms," Renewable Energy, Elsevier, vol. 138(C), pages 458-464.
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    3. Abdullahi Abubakar Mas’ud & Jorge Alfredo Ardila-Rey & Ricardo Albarracín & Firdaus Muhammad-Sukki & Nurul Aini Bani, 2017. "Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources," Energies, MDPI, vol. 10(7), pages 1-20, July.
    4. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Jamil, Furqan & Ali, Hafiz Muhammad & Nasir, Muhammad Ali & Karahan, Mehmet & Janjua, M.M. & Naseer, Ammar & Ejaz, Ali & Pasha, Riffat Asim, 2021. "Evaluation of photovoltaic panels using different nano phase change material and a concise comparison: An experimental study," Renewable Energy, Elsevier, vol. 169(C), pages 1265-1279.
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    Cited by:

    1. Aidong Zeng & Jiawei Wang & Yaheng Wan, 2023. "Coordinated Optimal Dispatch of Electricity and Heat Integrated Energy Systems Based on Fictitious Node Method," Energies, MDPI, vol. 16(18), pages 1-24, September.

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