Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model
In this study, the specific heat capacity of Alumina (Al₂O₃)/water nanofluid has been accurately evaluated using genetic algorithm/support vector regression (GA/SVR) model at volume fractions of 3.7–9.3%. The proposed (genetic algorithm/support vector regression) GA/SVR model was formulated using vo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier BV
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/81378/ http://psasir.upm.edu.my/id/eprint/81378/1/Modeling%20and%20prediction%20of%20the%20specific%20heat%20capacity%20of%20Al%E2%82%82O%E2%82%83water%20nanofluids%20using%20hybrid%20genetic%20algorithmsupport%20vector%20regression%20model.pdf |
| _version_ | 1848859089883365376 |
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| author | Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. |
| author_facet | Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. |
| author_sort | Alade, Ibrahim Olanrewaju |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In this study, the specific heat capacity of Alumina (Al₂O₃)/water nanofluid has been accurately evaluated using genetic algorithm/support vector regression (GA/SVR) model at volume fractions of 3.7–9.3%. The proposed (genetic algorithm/support vector regression) GA/SVR model was formulated using volume fractions and specific heat capacities of the alumina nanoparticles. The developed GA/SVR model is very accurate as determined from 99.998% correlation coefficient with experimentally obtained data and also has a root mean square error of 0.0014. Furthermore, the obtained results from the GA/SVR were compared with existing analytic models. Remarkably, the proposed model achieved an order of magnitude improvement over the model based on thermal equilibrium (Model II) and a two order of magnitude improvement over the model based on simple mixing rule for ideal gases (model I). Given the improvement in the accuracy, the proposed model would be useful for rapid and highly accurate estimation of the specific heat capacity of alumina/water nanofluids. |
| first_indexed | 2025-11-15T12:23:48Z |
| format | Article |
| id | upm-81378 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:23:48Z |
| publishDate | 2019 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-813782021-02-23T17:34:09Z http://psasir.upm.edu.my/id/eprint/81378/ Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. In this study, the specific heat capacity of Alumina (Al₂O₃)/water nanofluid has been accurately evaluated using genetic algorithm/support vector regression (GA/SVR) model at volume fractions of 3.7–9.3%. The proposed (genetic algorithm/support vector regression) GA/SVR model was formulated using volume fractions and specific heat capacities of the alumina nanoparticles. The developed GA/SVR model is very accurate as determined from 99.998% correlation coefficient with experimentally obtained data and also has a root mean square error of 0.0014. Furthermore, the obtained results from the GA/SVR were compared with existing analytic models. Remarkably, the proposed model achieved an order of magnitude improvement over the model based on thermal equilibrium (Model II) and a two order of magnitude improvement over the model based on simple mixing rule for ideal gases (model I). Given the improvement in the accuracy, the proposed model would be useful for rapid and highly accurate estimation of the specific heat capacity of alumina/water nanofluids. Elsevier BV 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81378/1/Modeling%20and%20prediction%20of%20the%20specific%20heat%20capacity%20of%20Al%E2%82%82O%E2%82%83water%20nanofluids%20using%20hybrid%20genetic%20algorithmsupport%20vector%20regression%20model.pdf Alade, Ibrahim Olanrewaju and Abd Rahman, Mohd Amiruddin and Saleh, Tawfik A. (2019) Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model. Nano-Structures and Nano-Objects, 17. pp. 103-111. ISSN 2352-507X https://www.sciencedirect.com/science/article/pii/S2352507X17301506 10.1016/j.nanoso.2018.12.001 |
| spellingShingle | Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model |
| title | Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model |
| title_full | Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model |
| title_fullStr | Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model |
| title_full_unstemmed | Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model |
| title_short | Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model |
| title_sort | modeling and prediction of the specific heat capacity of al₂o₃/water nanofluids using hybrid genetic algorithm/support vector regression model |
| url | http://psasir.upm.edu.my/id/eprint/81378/ http://psasir.upm.edu.my/id/eprint/81378/ http://psasir.upm.edu.my/id/eprint/81378/ http://psasir.upm.edu.my/id/eprint/81378/1/Modeling%20and%20prediction%20of%20the%20specific%20heat%20capacity%20of%20Al%E2%82%82O%E2%82%83water%20nanofluids%20using%20hybrid%20genetic%20algorithmsupport%20vector%20regression%20model.pdf |