LOADING CAPACITY IN
Database,
Correlations & Analysis
Simon Piché, Faïçal Larachi,*Bernard P.A. Grandjean
Department
of Chemical Engineering & CERPIC
Laval
University, Québec, Canada G1K 7P4
Chem. Eng. & Technol., 24, 373-380 (2001)
Errata: U5 in Table 5 should be read as:
Abstract: Experimental results
published in the literature between 1935 and 2000 were used to generate a
working database of 558 loading capacity data for randomly dumped packed
beds. The reported measurements were first used to review the accuracy of
the few available predicting loading capacity correlations. The Billet
and Schultes semi-empirical correlation (Trans IChemE., 77, 1999, p. 498)
emerged as the best prediction method and is recommended for loading transition
estimation, only when the constant of a given packing element is
available. When such a model-dependent parameter is unavailable, an
alternative and generalized neural network correlation is proposed to improve
the broadness and accuracy in predicting the loading capacity for packed
towers. A combination of five dimensionless groups, namely the liquid
Reynolds (ReL), Galileo (GaL) and Stokes (StL) numbers as well as the packing
sphericity (N) and one bed number (SB) outlining the tower
dimensions were used as inputs of the neural network correlation for the
prediction of the loading capacity via the Lockhart-Martinelli parameter (i). The correlation
yielded an absolute average relative error of 21% and a standard deviation of
19.9%. Through a sensitivity analysis, the Stokes number in the liquid phase
exhibits the strongest influence on the prediction while the liquid velocity,
gas density and packing surface area are the leading physical properties
defining the loading level.
Keywords:
randomly packed bed, counter-current flow, loading capacity, neural network,
database
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