An approach to predict the isobaric specific heat capacity of nitrides/ethylene glycol-based nanofluids using support vector regression

•SVR model for specific heat capacity of nitrides/EG-based nanofluids is developed.•SVR model parameters were optimized using the Bayesian optimization algorithm.•The model inputs are mass fraction, molecular mass of NPs, temp & size of NPs.•Proposed model exhibits improved prediction accuracy c...

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Vydané v:Journal of energy storage Ročník 29; s. 101313
Hlavní autori: Alade, Ibrahim Olanrewaju, Rahman, Mohd Amiruddin Abd, Saleh, Tawfik A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.06.2020
Predmet:
Bayesian algorithm
Ethylene-glycol
Nanofluid
Nanoparticles
Nitrides
Support vector regression
Nanoparticles
Nitrides
Support vector regression
Bayesian algorithm
Nanofluid
Ethylene-glycol
ISSN:2352-152X, 2352-1538
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Shrnutí:•SVR model for specific heat capacity of nitrides/EG-based nanofluids is developed.•SVR model parameters were optimized using the Bayesian optimization algorithm.•The model inputs are mass fraction, molecular mass of NPs, temp & size of NPs.•Proposed model exhibits improved prediction accuracy compared to existing models. This study presents a novel strategy based on Bayesian support vector regression for the estimation of the specific heat capacity of nitrides/ethylene glycol-based nanofluid. The nanoparticles considered are aluminium nitride (AlN), silicon nitride (Si3N4) and titanium nitride (TiN). The proposed model was built using simple and easy-to-obtain inputs such as the size of the nanoparticles (20, 30, 50, and 80 nm), the molar mass of the nanoparticles, mass fraction of nanoparticles (0.01 - 0.1) and the temperature (288.15 K, 298.15 K, and 308.15 K). Our suggested model showed better prediction accuracy over the analytical models for the estimation of specific heat capacity of nitrides/ethylene glycol nanofluids. Given the simplicity of the model inputs and the accuracy of the model, the approach presented provides a more reliable prediction of specific heat capacity of nitrides-ethylene glycol-based nanofluids than previous models.
ISSN:2352-152X
2352-1538
DOI:10.1016/j.est.2020.101313