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, DOI https://doi.org/10.3923/jas.2009.3196.3198.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Additional Information: | Cited By (since 1996): 2 Export Date: 5 March 2013 Source: Scopus Language of Original Document: English Correspondence Address: Ibrehem, A. S.; Department of Chemical Engineering, University of Malaya, 50603, Kuala Lurnpur, Malaysia References: Akhtar, A., Pareek, V., Tade, M., CFD simulations for continuous flow of bubbles through gas-liquid columns: Application of VOF method (2007) Chem. Prod. Process Model., 2, pp. 1-19; Bahavraju, S.M., Russel, T.W.F., Blanch, H.W., The design of gas sparged devices for viscous liquid systems (1978) AICHE J., 24, pp. 454-466; Bhat, N., McAvoy, T.J., Use of neural nets for dynamic modeling and control of chemical process systems (1990) Comput. Chem. Eng., 14, pp. 573-582; Cai, S., Toral, H., Qiu, J., Archer, J.S., Network based objective flow regime identification in air-water system two phase flow (1994) Can. J. Chem. Eng., p. 72; Deckwer, W.D., Schumpe, A., Improved tools for bubble column reactor design and scale-up (1993) Chem. Eng. Sci., 48, pp. 889-911; Degaleesan, S., Dudukovic, M., Pan, Y., Experimental study of gas-induced liquid-flow structures in bubble columns (2001) AIChE J., 47, pp. 1913-1931; Kantarci, N., Borak, F., Ulgen, K.O., Bubble column reactors (2005) Process Biochem., 40, pp. 2263-2283; Lendaris, G., (2004) Supervised Learning in ANN from Introduction to Artificial Intelligence, p. 7. , New York, April; Leonard, J., Kramer, M.A., Improvement of the back-propagation algorithm for training neural networks (1990) Comp. Chem. Eng., 14, pp. 337-341; Lippmann, R.P., An introduction to computing with neural nets (1987) IEEE Trans. ASSP Mag., 4, pp. 4-22 |
| Uncontrolled Keywords: | ANN; Bubble columns; Bubble size. |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TP Chemical technology |
| Divisions: | Faculty of Engineering |
| Depositing User: | Mr Jenal S |
| Date Deposited: | 10 Jul 2013 02:35 |
| Last Modified: | 10 Feb 2021 03:47 |
| URI: | http://eprints.um.edu.my/id/eprint/7029 |
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