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

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 na...

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Main Authors: Alade, Ibrahim Olanrewaju, Abd Rahman, Mohd Amiruddin, Abbas, Zulkifly, Yaakob, Yazid, A. Saleh, Tawfik
Format: Article
Language:English
Published: Elsevier 2020
Online Access:http://psasir.upm.edu.my/id/eprint/87915/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/87915/
https://www.sciencedirect.com/science/article/pii/S0038092X19312812
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spelling my.upm.eprints.879152022-06-03T05:11:40Z http://psasir.upm.edu.my/id/eprint/87915/ Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Abbas, Zulkifly Yaakob, Yazid A. Saleh, Tawfik 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. Elsevier 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87915/1/ABSTRACT.pdf Alade, Ibrahim Olanrewaju and Abd Rahman, Mohd Amiruddin and Abbas, Zulkifly and Yaakob, Yazid and A. Saleh, Tawfik (2020) Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide. Solar Energy, 197. 485 - 490. ISSN 0038-092X https://www.sciencedirect.com/science/article/pii/S0038092X19312812 10.1016/j.solener.2019.12.067
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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.
format Article
author Alade, Ibrahim Olanrewaju
Abd Rahman, Mohd Amiruddin
Abbas, Zulkifly
Yaakob, Yazid
A. Saleh, Tawfik
spellingShingle Alade, Ibrahim Olanrewaju
Abd Rahman, Mohd Amiruddin
Abbas, Zulkifly
Yaakob, Yazid
A. Saleh, Tawfik
Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide
author_facet Alade, Ibrahim Olanrewaju
Abd Rahman, Mohd Amiruddin
Abbas, Zulkifly
Yaakob, Yazid
A. Saleh, Tawfik
author_sort Alade, Ibrahim Olanrewaju
title Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide
title_short Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide
title_full Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide
title_fullStr Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide
title_full_unstemmed Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide
title_sort application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide
publisher Elsevier
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/87915/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/87915/
https://www.sciencedirect.com/science/article/pii/S0038092X19312812
_version_ 1735388293465374720
score 13.211869

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