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International Journal of Chemical Reactor Engineering

Ed. by de Lasa, Hugo / Xu, Charles Chunbao

12 Issues per year


IMPACT FACTOR 2017: 0.881
5-year IMPACT FACTOR: 0.908

CiteScore 2017: 0.86

SCImago Journal Rank (SJR) 2017: 0.306
Source Normalized Impact per Paper (SNIP) 2017: 0.503

Online
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1542-6580
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Volume 6, Issue 1

Prediction of the Heat Transfer Coefficient in a Bubble Column Using an Artificial Neural Network

Adel A. Al-Hemiri / Nada S. Ahmedzeki
Published Online: 2008-08-19 | DOI: https://doi.org/10.2202/1542-6580.1655

An artificial neural network (ANN) was applied for the prediction of the heat transfer coefficient in bubble columns, in order to obtain a general model and to facilitate the scale up of these multiphase contactors, covering a wide range of operating conditions, physical properties, and column dimensions, obtained from literature. A large number of data was collected (more than 1000) via a comprehensive literature survey. Selected parameters affecting the heat transfer coefficient were organized in six groups to serve as the input parameters. These were: gas superficial velocity, gas density, liquid density, diameter of the column, liquid viscosity, and gas hold-up. Four Back-Propagation Networks (BPNNS) were built. Two were trained using a different number of input parameters. The first ANN was trained with six inputs, which were the aforementioned parameters. The second was trained with three inputs only. These were gas velocity, liquid viscosity and gas hold-up. Each ANN was examined for two structures i.e., one hidden layer and two hidden layers. Comparison between these networks was made to find the optimal ANN structure with minimum %AARE and the maximum correlation coefficient (%R). It was found that the ANN structure of [6-13-1] with a %AARE of 16.2 and a %R of 94 was the best.

Keywords: bubble columns; heat transfer; ANN

About the article

Published Online: 2008-08-19


Citation Information: International Journal of Chemical Reactor Engineering, Volume 6, Issue 1, ISSN (Online) 1542-6580, DOI: https://doi.org/10.2202/1542-6580.1655.

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