Prediction of bubble size in bubble columns using artificial neural network
In the literature, several correlations have been proposed for bubble size pre-diction in bubble columns. However, these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for...
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Journal of Applied Sciences
2009
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my.um.eprints.70292021-02-10T03:47:33Z http://eprints.um.edu.my/7029/ Prediction of bubble size in bubble columns using artificial neural network Ibrehem, A.S. Hussain, Mohd Azlan TA Engineering (General). Civil engineering (General) TP Chemical technology In the literature, several correlations have been proposed for bubble size pre-diction in bubble columns. However, these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for bubble sizes in the homogenous region in bubble columns was derived using Artificial Neural Network (ANN) modeling. The bubble diameter was found to be a function of six parameters: gas velocity, column diameter, diameter of orifice, liquid density, liquid viscosity and liquid surface tension. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 7.3 and correlation coefficient of 92.2. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of bubble sizes. The developed correlation also shows better prediction over a wide range of operation parameters in bubble columns. Journal of Applied Sciences 2009 Article PeerReviewed Ibrehem, A.S. and Hussain, Mohd Azlan (2009) Prediction of bubble size in bubble columns using artificial neural network. Journal of Applied Sciences, 9 (17). pp. 3196-3198. ISSN 1812-5654 http://www.scopus.com/inward/record.url?eid=2-s2.0-68949203218&partnerID=40&md5=201807d768cd010a8e0fae5999e42e38 10.3923/jas.2009.3196.3198 |
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TA Engineering (General). Civil engineering (General) TP Chemical technology Ibrehem, A.S. Hussain, Mohd Azlan Prediction of bubble size in bubble columns using artificial neural network |
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In the literature, several correlations have been proposed for bubble size pre-diction in bubble columns. However, these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for bubble sizes in the homogenous region in bubble columns was derived using Artificial Neural Network (ANN) modeling. The bubble diameter was found to be a function of six parameters: gas velocity, column diameter, diameter of orifice, liquid density, liquid viscosity and liquid surface tension. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 7.3 and correlation coefficient of 92.2. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of bubble sizes. The developed correlation also shows better prediction over a wide range of operation parameters in bubble columns. |
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Article |
author |
Ibrehem, A.S. Hussain, Mohd Azlan |
author_facet |
Ibrehem, A.S. Hussain, Mohd Azlan |
author_sort |
Ibrehem, A.S. |
title |
Prediction of bubble size in bubble columns using artificial neural network |
title_short |
Prediction of bubble size in bubble columns using artificial neural network |
title_full |
Prediction of bubble size in bubble columns using artificial neural network |
title_fullStr |
Prediction of bubble size in bubble columns using artificial neural network |
title_full_unstemmed |
Prediction of bubble size in bubble columns using artificial neural network |
title_sort |
prediction of bubble size in bubble columns using artificial neural network |
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Journal of Applied Sciences |
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2009 |
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http://eprints.um.edu.my/7029/ http://www.scopus.com/inward/record.url?eid=2-s2.0-68949203218&partnerID=40&md5=201807d768cd010a8e0fae5999e42e38 |
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