Abstract
In the literature, several correlations have been proposed for bubble size prediction 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.
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.