TECHNICAL PAPERS
Apr 15, 2002

Neural Network Prediction of Air Stripping KLa

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Publication: Journal of Environmental Engineering
Volume 128, Issue 5

Abstract

Design of air stripping packed towers used to remove volatile organic compounds requires an estimate of the overall mass transfer coefficient (KLa), which is frequently obtained via physically based parametric correlations. Parametric correlations have some shortcomings and produce predictions with relatively large deviations for full-scale, modern application of air stripping towers. In this study, neural network (NN) technology, a powerful new nonparametric approach, is used to analyze mass transfer characteristics in air stripping towers and to simulate KLa. A large database that is representative of current applications of air stripping towers was assembled for this analysis. The KLa predictions by neural networks were superior to both the Onda model [Onda, K., Takeuchi, H., and Okumoto, Y. (1968). “Mass transfer between gas and liquid phases in packed columns.” J. Chem. Eng. Jpn., 1, 56–62.] and an improved Onda model [Djebber, Y. and Narbaitz, R. M. (unpublished)], the best existing parametric models for air stripping applications. The average absolute error for the validation, as well as for the development data, were found to be less than 19%. The NN model was able to simulate the sudden increase in KLa at high gas loading rates. Also, it simulated more realistically the effect of the packing depth and liquid flow.

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Go to Journal of Environmental Engineering
Journal of Environmental Engineering
Volume 128Issue 5May 2002
Pages: 451 - 460

History

Received: Sep 27, 2000
Accepted: Jul 11, 2001
Published online: Apr 15, 2002
Published in print: May 2002

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Authors

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Y. Djebbar
Senior Project Engineer, Greater Vancouver Regional District, 4330 Kingsway, Burnaby, BC, Canada V5H 4G8.
R. M. Narbaitz
Professor, University of Ottawa, Civil Engineering Dept., Ottawa, ON, Canada KIN 6N5.

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