Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide

•The specific heat capacity (SCH) of CuO/water nanofluids was accurately modelled using SVR and ANN models.•The SVR model has a higher prediction accuracy than the ANN model.•Both SVR and ANN models performed much better than the existing empirical relations. This paper presents the modelling of the...

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Bibliographic Details
Published in:Solar energy Vol. 197; pp. 485 - 490
Main Authors: Alade, Ibrahim Olanrewaju, Abd Rahman, Mohd Amiruddin, Abbas, Zulkifly, Yaakob, Yazid, Saleh, Tawfik A.
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.02.2020
Pergamon Press Inc
Subjects:
Artificial neural network
Artificial neural networks
Bayesian algorithm
Copper oxides
Heat
Heat transfer
Learning algorithms
Learning theory
Machine learning
Model accuracy
Nanofluids
Nanoparticles
Neural networks
Regression analysis
Solar energy
Specific heat
Specific heat capacity
Support vector machines
Support vector regression
Support vector regression
Bayesian algorithm
Artificial neural network
Specific heat capacity
Nanofluids
ISSN:0038-092X, 1471-1257
Online Access:Get full text
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Summary:•The specific heat capacity (SCH) of CuO/water nanofluids was accurately modelled using SVR and ANN models.•The SVR model has a higher prediction accuracy than the ANN model.•Both SVR and ANN models performed much better than the existing empirical relations. This paper presents the modelling of the specific heat capacity (SHC) of CuO/water nanofluids using a support vector regression (SVR) and artificial neural network models (ANN). The models presented were developed from the experimental data of SCH of CuO nanoparticles, the volume fractions of CuO nanoparticles and fluid temperature. The volume fraction of CuO nanoparticles considered ranges from 0.4 to 2% while the temperature range includes 293–338 K. The results obtained revealed that the SVR model exhibits slightly higher accuracy compared to the ANN model. However, both the SVR and ANN models clearly demonstrate better prediction performance for the SHC of CuO/water nanofluids compared to the existing theoretical models. The results obtained in this study proves that machine learning models provide a more accurate estimation of SHC of CuO/water nanofluids and they are recommended for heat transfer calculations due to their superior accuracy.
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ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2019.12.067