Статья

Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems

Tao HaiArtificial Intelligence Research Center (AIRC), Ajman University, P.O. Box 346, Ajman, UAEAli BasemFaculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, IraqAs’ad AlizadehDepartment of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, IraqKamal SharmaInstitute of Engineering and Technology, GLA University, Mathura, U.P, 281406, IndiaDheyaa J. JasimHusam RajabCollege of Engineering, Department of Mechanical Engineering, Najran University, King Abdulaziz Road, P.O Box 1988, Najran, Kingdom of Saudi ArabiaAbdelkader MabroukDepartment of Civil Engineering, College of Engineering, Northern Border University, Arar, 73222, Saudi ArabiaLioua KolsiDepartment of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il City, 81451, Saudi ArabiaWajdi RajhiDepartment of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il City, 81451, Saudi ArabiaHamid MalekiDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran. [email protected]Narinderjit Singh Sawaran SinghFaculty of Data Science and Information Technology, INTI International University, Nilai, 71800, Malaysia

2024en

ABI

Аннотация

Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature. The optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), and thermal conductivity (TC). In the proposed hybrid approach, the powerful group method of data handling (GMDH)-type ANN technique is used to model TPPs in terms of input variables. The obtained models are integrated into the multi-objective particle swarm optimization (MOPSO) and multi-objective thermal exchange optimization (MOTEO) algorithms, forming a three-objective optimization problem. In the final step, the TOPSIS technique, one of the well-known multi-criteria decision-making (MCDM) approaches, is employed to identify the desirable Pareto points. Modeling results showed that the developed models for TC, DV, and SHC demonstrate a strong performance by R-values of 0.9984, 0.9985, and 0.9987, respectively. The outputs of MOPSO revealed that the Pareto points dispersed a broad range of MXene MFs (0-0.4%). However, the temperature of these optimal points was found to be constrained within a narrow range near the maximum value (75 °C). In scenarios where TC precedes other objectives, the TOPSIS method recommended utilizing an MF of over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt for an MF ranging from 0.15 to 0.17%. Also, when SHC becomes the primary concern, TOPSIS advised utilizing the base fluid without any MXene additive.

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Идентификаторы

DOI: 10.1038/s41598-024-81044-3

Цитирования и источники

Цитирований: 5Использованных источников: 0

Показатели — AkademScholar