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

Hai T 1 , Basem A 2 , Alizadeh A 3 , Sharma K 4 , Jasim DJ 5 , Rajab H 6 Show all authors , Mabrouk A 7 , Kolsi L 8 , Rajhi W 8 , Maleki H 9 , Sawaran Singh NS 10

Affiliations 

  • 1 State Key Laboratory of Public Big Data, Guizhou University, Guizhou Guiyang, 550025, China
  • 2 Faculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq
  • 3 Department of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, Iraq
  • 4 Institute of Engineering and Technology, GLA University, Mathura, U.P, 281406, India
  • 5 Department of Petroleum Engineering, Al-Amarah University College, Maysan, Iraq
  • 6 College of Engineering, Department of Mechanical Engineering, Najran University, King Abdulaziz Road, P.O Box 1988, Najran, Kingdom of Saudi Arabia
  • 7 Department of Civil Engineering, College of Engineering, Northern Border University, Arar, 73222, Saudi Arabia
  • 8 Department of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il City, 81451, Saudi Arabia
  • 9 Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran. [email protected]
  • 10 Faculty of Data Science and Information Technology, INTI International University, Nilai, 71800, Malaysia
Sci Rep, 2024 Nov 27;14(1):29524.
PMID: 39604527 DOI: 10.1038/s41598-024-81044-3

Abstract

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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