Synthesis, stability, density, viscosity of ethylene glycol-based ternary hybrid nanofluids: Experimental investigations and model -prediction using modern machine learning techniques

A direct sol-gel technique was utilized to produce rGO-Fe3O4-TiO2 ternary hybrid nanocomposites to produce ethylene glycol (EG) based stable nanofluids, characterized by energy-dispersive X-ray, X-ray dispersion, Fourier transform infrared spectroscopy, scanning electron microscopy, and zeta potenti...

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Published in:Powder technology 2022-03, Vol.400, p.117190, Article 117190
Main Authors: Said, Zafar, Cakmak, Nese Keklikcioglu, Sharma, Prabhakar, Sundar, L. Syam, Inayat, Abrar, Keklikcioglu, Orhan, Li, Changhe
Format: Article
Language:English
Subjects:
Analytical methods
Artificial Neural Networks
Density
Dispersion
Ethylene
Ethylene glycol
Fourier analysis
Fourier transforms
Heat transfer
Hybrid nanofluid
Infrared analysis
Infrared spectroscopy
Iron oxides
Learning algorithms
Machine learning
Nanocomposites
Nanofluids
Nanoparticles
Neural networks
Prediction models
Predictive modeling
Regression analysis
Scanning electron microscopy
Sol-gel processes
Stability
Statistical analysis
Support vector machines
Ternary nanocomposite
Titanium dioxide
Uncertainty
Viscosity
Zeta potential
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Summary:A direct sol-gel technique was utilized to produce rGO-Fe3O4-TiO2 ternary hybrid nanocomposites to produce ethylene glycol (EG) based stable nanofluids, characterized by energy-dispersive X-ray, X-ray dispersion, Fourier transform infrared spectroscopy, scanning electron microscopy, and zeta potential. Viscosity and density analysis were investigated by varying temperatures (25 to 50 °C), and wt% (0.01 to 0.25). For 0.25 wt% at 50 °C, density increased by 2.45%, and viscosity by 133.5%. The development of a prediction model by processing the variational parameters with machine learning and studying properties such as characterization, stability, and density of rGO-Fe3O4-TiO2 hybrid nanofluids has provided an unprecedented study in the literature. The nonlinear nature and volume of data generated by the subsequent experimental study were difficult to model using traditional analytical methods. As a result, for the creation of prognostic models, advanced machine learning techniques such as Boosted Regression Tree (BRT), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) was applied. These prediction models' prognostic skills and uncertainty were assessed using statistical indices, Theil's statistics, and Taylor's diagram. The R-value for the BRT-based density (0.9989) and viscosity (0.9979) prediction models was higher than that of the ANN-based and SVM-based prediction models. In developed density models, Theil's U2 uncertainty was as low as 0.0689, 0.0775, and 0.0981 for BRT, ANN, and SVM, respectively. As a conclusion, it is stated that BRT, ANN, and SVM can accurately imitate the laboratory-based assessment of density and viscosity values of ternary hybrid nanofluids over a wide temperature and nanoparticle concentration ratio range. On the other hand, the BRT was marginally better than ANN but much better than SVM. The current study's findings are appropriate for applications needing long-term stability and improved heat transfer performance. [Display omitted] •Novel ternary rGO-Fe3O4-TiO2 nanocomposites synthesized.•Hybrid nanofluid at 0.01 wt% showed +63.45 mV value of zeta potential.•Viscosity increased by 64.5% at 25 °C for 0.25 wt% of rGO-Fe3O4-TiO2 nanofluids.•Statistical indices and Tayor's diagrams were used to compare efficacy of ANN, BRT, and SVM.•Theil's U2 uncertainty in developed BRT models was as low as 0.121 for viscosity models.
ISSN:0032-5910
1873-328X
DOI:10.1016/j.powtec.2022.117190