Neural Network Prediction of Air Stripping KLa

Abstract

Design of air stripping packed towers used to remove volatile organic compounds requires an estimate of the overall mass transfer coefficient (\IK\dLa\N), 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 \IK\dLa\N. A large database that is representative of current applications of air stripping towers was assembled for this analysis. The \IK\dLa\N 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.” \IJ. Chem. Eng. Jpn.\N, 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 and an improved Onda model [Djebber, Y. and Narbaitz, R. M. (unpublished)], the best existing parametric models for air stripping applications.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 \IK\dLa\N at high gas loading rates. Also, it simulated more realiistically the effect of the packing depth and liquid flow.