Interfacial Mass Transfer in Randomly Packed Towers:
A Confident Correlation for Environmental Applications

Simon Piché, Bernard P.A. Grandjean*, Ion Iliuta, Faïçal Larachi
Department of Chemical Engineering & CERPIC
Laval University, Québec, Canada G1K 7P4

Environmental Science & Technology, 35, 4817-4822 (2001)

Abstract ¾Volumetric mass transfer coefficients (kLaw, KLaw, kGaw, KGaw) required for randomly dumped packed tower design were gathered from the literature to generate a working database comprehending 2,675 measurements relevant to water and air pollution abatement processes. The cross-examination of two important correlations predicting mass transfer coefficients was achieved through this database (Onda correlation, 1968; Billet and Schultes correlation, 1993). Some limitations regarding their accuracy level came to light. Artificial neural network (ANN) modeling is then proposed to improve the accuracy in predicting all four mass transfer coefficients. A sole and robust ANN correlation was built to predict the dimensionless gas (or liquid) film Sherwood number  as a function of a combination of six dimensionless groups, namely the liquid Reynolds , Froude , Eotvös  numbers, the gas (or liquid) Schmidt number , the Lockhart-Martinelli parameter  and a bed-characterizing number . Using the ANN correlation along with the two-film theory, a reconciliation procedure was also implemented resulting in accurate predictions of the gas (or liquid) overall (or film) volumetric mass transfer coefficients. The correlation yielded an absolute average relative error of 22.1%; a standard deviation of 21.1% based on whole database and the ANN predictions remain in accordance with the physical evidence reported in the literature.

You can get the  file that contains an Excel worksheet simulator to compute pressure drop, liquid holdup, loading/flooding capacities, film and overall volumetric mass transfer  coefficients.

You may also download our 
Excel worksheets simulators for  Trickle-bed or Flooded Bed reactors.

The neural correlation was developped with the software NNFit