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

Enas Taha Sayed (), Hegazy Rezk (), Abdul Ghani Olabi (), Mohamed R. Gomaa, Yahia B. Hassan, Shek Mohammad Atiqure Rahman, Sheikh Khaleduzzaman Shah () and Mohammad Ali Abdelkareem ()
Additional contact information
Enas Taha Sayed: Center for Advanced Materials Research, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
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
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

Energies, 2022, vol. 15, issue 22, 1-13

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.

Keywords: photovoltaic thermal (PVT); phase change material; nanofluid; optimization; modelling; exergy; thermal energy (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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