Abstract
Existing parametric correlations have been found to have difficulties in predicting the removal of trace levels of volatile organic chemicals by modern air stripping towers. In this study, a new approach using a nonparametric kernel regression method was used to predict the mass transfer coefficient, KLa, of air stripping towers. Although only four variables were used, the predictions are already improved more than 50% as compared with Onda correlation, the best existing parametric correlation. The proposed technique shows a dependency of KLa on the liquid flow rate which is in good agreement with established theory. Previous parametric approaches were unable to model this relationship correctly. Key words: mass transfer coefficient, air stripping tower, volatile organic compound, nonparametric kernel regression.
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