IMPROVED LIQUID HOLD-UP
FOR PACKED TOWERS
Simon Piché, Faïçal Larachi,*Bernard P.A. Grandjean
Department of Chemical Engineering & CERPIC
Laval University, Québec, Canada G1K 7P4
Chem. Eng. Res. Des. (Trans IChemE part A), 79, 71-80 (2001)
Abstract: The state-of-the-art tools for the evaluation of the total liquid holdup in gas-liquid counter-current randomly dumped packed beds are critically evaluated by thoroughly interrogating a wide hydrodynamic database. This database consisting of ca. 1,500 experiments on liquid hold-up below the flooding point represents an important portion of the non-proprietary information released in the literature since the 1930s. Providing access to diversified information, it is dedicated to embrace wide-ranging temperature and gas density levels, and packing shapes extending from the classical ones to the modern third generation packings. Furthermore, a total of eleven correlations on the total liquid hold-up extracted from the literature are cross-examined with the database. Many limitations, regarding the level of accuracy and generalization, come to light with this investigation. Artificial neural network modeling and dimensional analysis are then proposed to improve the accuracy in predicting the total liquid hold-up in the pre-loading and the loading regions of packed beds. A combination of five dimensionless groups, comprising the liquid Reynolds (ReL), Froude (FrL) and Ohnesorge (OhL) numbers as well as the gas Froude (FrG) and Stokes (StG) numbers are used as the basis of the correlation. The correlation yields an absolute average relative error of ca. 14 % for the whole database and remains in accordance with the trends reported in the literature.
randomly packed bed, counter-current flow, liquid hold-up, neural network,
You can get the packedbedsimulator.zip file that contains an Excel worksheet simulator to compute pressure drop, liquid holdup along with loading and flooding capacities.
You may also download our Excel worksheets simulators for Trickle-bed or Flooded Bed reactors.
The neural correlation was developped with the software NNFit