THREE-PHASE FLUIDIZATION
MACROSCOPIC HYDRODYNAMICS – REVISITED
Faïçal Larachi*, Lamia
Belfares, Ion Iliuta, Bernard P.A. Grandjean
Department
of Chemical Engineering & CERPIC,
Laval
University, Québec, Canada G1K 7P4
*Corresp. author. Phone:
1-418-656-3566;
Fax:
1-418-656-5993. email flarachi@gch.ulaval.ca
Industrial & Engineering Chemistry Research; 2001;
40(3); 993-1008.
Errata:
- In Table 1 and Glossary, coalescence index CI=1 for
coalescing liquid and CI=2 for noncoalescing
liquid
- In Table 3, J=14
- In Table 4: Input, Output and Fitting Parameters of the ANN-DA
Correlation for EL
Abstract:
The
state-of-the-art tools for the evaluation of the macroscopic hydrodynamics of
co-current upflow three-phase fluidization are
critically evaluated by thoroughly interrogating the broadest fluidization
database ever built. The database is compiled through worldwide conjoint
initiatives as a result of a decade of compilation efforts by the groups of professors L. S. Fan (Ohio,
USA), S. D. Kim (Seoul, South Korea),
G. Wild (Nancy, France) and our ULaval
group. The database represents almost the whole heritage of the
non-proprietary data released in the open literature in the field of
gas-liquid-solid fluidization (23,000 experiments on bed porosity, liquid, gas
and solids hold-ups). It is dedicated to embrace wide-ranging fluids’
properties, particle and vessel sizes, and operating conditions. The
database contains 55 Newtonian (20,500 data), 19 non-Newtonian liquids (2,500
data), 110 various particles, 17 different column diameters, and includes wall
effect ratios Dc/dp and grain sizes ranging,
respectively, from 8 to 800 and 0.25 to 15 mm. Two novel approaches in
the field of three-phase fluidization modeling are proposed to reconcile the
formidable diversity of patterns and the wide variability of hydrodynamic
parameters encountered in this advanced database. Both of them exhibit a
substantial gain in their forecasting ability with respect to the currently
known prediction methods. The first approach relies on the combination of
multi-layer perceptron artificial neural networks and
dimensional analysis (ANN-DA approach) to derive three highly accurate
correlations for bed porosity, and liquid and gas hold-ups. The second is
based on a phenomenological hybrid k-x generalized bubble wake model (k-x GBWM)
in which the wake parameters k and x are beforehand extracted by solving an inverse
k-x GBW model. The ANN-DA approach is then applied to correlate k and x
in terms of the accessible fluidization input characteristics, and fed into the
k-x GBWM to forecast the phase holdups. The robustness of the proposed
ANN-DA correlations and k-x GBWM is assessed and the limitations of the
correlations with regard to their generalization capabilities are discussed.
Keywords : Three-phase fluidization, generalized bubble wake
model, hybrid modeling, neural network, database, hydrodynamics
You can get the simfl3p.zip
file (version 28/06/2001) that contains an Excel worksheet simulator to compute
phase hold-ups and bubble wake model parameters.
You may also download our Excel worksheets simulators for Trickle-bed or Flooded
Bed reactors.
The neural
correlation was developped with the software NNFit