ArticleScientific reports2024
Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Design of a Li-ion battery cooling system incorporating PCM, heat pipes, and liquid circuits using marine predator algorithm-enhanced ANN and multi-verse optimization.Scientific reports · 2026Article
- Intelligent multi-objective optimization of thermal comfort and ventilation performance in stratum ventilation design.Scientific reports · 2026Article
- Pareto-based design of thermophotovoltaic micro-combustors via a novel framework combining IGWO-tuned ANN, multi-objective multi-verse optimization, and ARAS-based decision making.Scientific reports · 2026Article
- Adaptive dynamic hypergraph learning for ingredient aware food recommendation.Scientific reports · 2025Article
- Integrated neural network and metaheuristic algorithms for balancing electrical performance and thermal safety in PEMFC design.Scientific reports · 2025Article
- Analysis of tunneling conductivity for MXene polymer system by the network of interphase: parametric examinations and experimental validation.Scientific reports · 2025Article
- Optimizing MXene graphene based fluids for solar energy conversion and storage using a novel intelligent framework.Scientific reports · 2025Article
- Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II.Scientific reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
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.
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