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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/87915/ http://psasir.upm.edu.my/id/eprint/87915/1/ABSTRACT.pdf |
| _version_ | 1848860522443702272 |
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| author | Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Abbas, Zulkifly Yaakob, Yazid A. Saleh, Tawfik |
| author_facet | Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Abbas, Zulkifly Yaakob, Yazid A. Saleh, Tawfik |
| author_sort | Alade, Ibrahim Olanrewaju |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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 293β338 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. |
| first_indexed | 2025-11-15T12:46:34Z |
| format | Article |
| id | upm-87915 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:46:34Z |
| publishDate | 2020 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 293β338 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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/87915/ http://psasir.upm.edu.my/id/eprint/87915/ http://psasir.upm.edu.my/id/eprint/87915/ http://psasir.upm.edu.my/id/eprint/87915/1/ABSTRACT.pdf |