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Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm

Nanofluids are now considered the most essential constituent of solar thermal collector due to their superior thermal performance over conventional fluids. An accurate determination of the thermal efficiency of the solar collector depends on the value of the specific heat capacity of the nanofluid. So far...

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Main Authors: Ibrahim, Olanrewaju Alade, Abd Rahman, Mohd Amiruddin, A. Saleh, Tawfik
Format: Article
Language:English
Published: Elsevier 2019
Online Access:http://psasir.upm.edu.my/id/eprint/81784/1/20201028%20-%20Predicting%20the%20specific%20heat%20capacity%20of%20alumina_ethylene%20glycol%20nanofluids%20using%20support%20vector%20regression%20model%20optimized%20with%20Bayesian%20algorithm%20.pdf
http://psasir.upm.edu.my/id/eprint/81784/
https://www.sciencedirect.com/science/article/abs/pii/S0038092X19301860
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spelling my.upm.eprints.817842020-12-04T06:03:03Z http://psasir.upm.edu.my/id/eprint/81784/ Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm Ibrahim, Olanrewaju Alade Abd Rahman, Mohd Amiruddin A. Saleh, Tawfik Nanofluids are now considered the most essential constituent of solar thermal collector due to their superior thermal performance over conventional fluids. An accurate determination of the thermal efficiency of the solar collector depends on the value of the specific heat capacity of the nanofluid. So far, limited attention has been devoted towards accurate modelling of specific heat capacity of nanofluids CPnf despite their relevance in many solar energy-related applications. Surprisingly, there are only two main analytic models for estimating the CPnf in the literature. In most of the reports, these models have shown considerable inconsistencies for predicting the values of CPnf . Moreover, the modelling performance of these models necessitates the need to develop accurate models for the prediction of CPnf . Herein, a Bayesian support vector regression (BSVR) model is proposed to estimate the specific heat capacity of Al₂O₃/ethylene glycol nanofluid. The model proposed was trained on eighty-four (84) experimental datasets and its predictive accuracy was validated on seventeen (17) new test set. The BSVR model exhibits high accuracy as measured by the values of Pearson's correlation coefficient and the absolute average relative deviation (AARD) of 99.95% and 0.1888, respectively. Remarkably, the accuracy obtained from the proposed BSVR model is an order of magnitude better than existing theoretical models. The proposed technique and model will be useful towards a more reliable and accurate computation of the efficiency of solar collectors. Elsevier 2019-05-01 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81784/1/20201028%20-%20Predicting%20the%20specific%20heat%20capacity%20of%20alumina_ethylene%20glycol%20nanofluids%20using%20support%20vector%20regression%20model%20optimized%20with%20Bayesian%20algorithm%20.pdf Ibrahim, Olanrewaju Alade and Abd Rahman, Mohd Amiruddin and A. Saleh, Tawfik (2019) Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm. Solar Energy, 183. pp. 74-82. ISSN 0038-092X https://www.sciencedirect.com/science/article/abs/pii/S0038092X19301860 10.1016/j.solener.2019.02.060
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 Nanofluids are now considered the most essential constituent of solar thermal collector due to their superior thermal performance over conventional fluids. An accurate determination of the thermal efficiency of the solar collector depends on the value of the specific heat capacity of the nanofluid. So far, limited attention has been devoted towards accurate modelling of specific heat capacity of nanofluids CPnf despite their relevance in many solar energy-related applications. Surprisingly, there are only two main analytic models for estimating the CPnf in the literature. In most of the reports, these models have shown considerable inconsistencies for predicting the values of CPnf . Moreover, the modelling performance of these models necessitates the need to develop accurate models for the prediction of CPnf . Herein, a Bayesian support vector regression (BSVR) model is proposed to estimate the specific heat capacity of Al₂O₃/ethylene glycol nanofluid. The model proposed was trained on eighty-four (84) experimental datasets and its predictive accuracy was validated on seventeen (17) new test set. The BSVR model exhibits high accuracy as measured by the values of Pearson's correlation coefficient and the absolute average relative deviation (AARD) of 99.95% and 0.1888, respectively. Remarkably, the accuracy obtained from the proposed BSVR model is an order of magnitude better than existing theoretical models. The proposed technique and model will be useful towards a more reliable and accurate computation of the efficiency of solar collectors.
format Article
author Ibrahim, Olanrewaju Alade
Abd Rahman, Mohd Amiruddin
A. Saleh, Tawfik
spellingShingle Ibrahim, Olanrewaju Alade
Abd Rahman, Mohd Amiruddin
A. Saleh, Tawfik
Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm
author_facet Ibrahim, Olanrewaju Alade
Abd Rahman, Mohd Amiruddin
A. Saleh, Tawfik
author_sort Ibrahim, Olanrewaju Alade
title Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm
title_short Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm
title_full Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm
title_fullStr Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm
title_full_unstemmed Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm
title_sort predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with bayesian algorithm
publisher Elsevier
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/81784/1/20201028%20-%20Predicting%20the%20specific%20heat%20capacity%20of%20alumina_ethylene%20glycol%20nanofluids%20using%20support%20vector%20regression%20model%20optimized%20with%20Bayesian%20algorithm%20.pdf
http://psasir.upm.edu.my/id/eprint/81784/
https://www.sciencedirect.com/science/article/abs/pii/S0038092X19301860
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score 13.211869

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