PREDICTION OF MINIMUM
FLUIDIZATION VELOCITY IN
THREE-PHASE
FLUIDIZED-BED REACTORS
F. Larachi, I. Iliuta1, , O. Rival, B.P.A. Grandjean
Department
of Chemical Engineering & CERPIC, Laval University, Québec, Canada G1K 7P4
1Department of
Chemical Engineering, Faculty of Industrial Chemistry, University Politehnica of
Industrial & Engineering Chemistry Research 39, 563-572
(2000)
Abstract: Knowledge of the onset of fluidization is of
considerable relevance and the key to three-phase fluidized-bed reactors design
and safe operation. Accordingly, using a wide historic ULmf database set up from the open literature, all the
quantification methods proposed to predict the minimum fluidization liquid
velocity in three-phase fluidized beds have been thoroughly revisited and
critically evaluated herein. The database, providing access to
diversified information related to over 540 measurements, is dedicated to
embrace wide-ranging fluids and bed properties. It covers 30 various
particles and 19 liquids and includes data such as aspect ratio, wall effect
(or column-to-particle diameter) ratio, and ReLmf
ranging from 0.8 to 27, 9 to 127, and 10-2 to 800, respectively. Indeed,
the ULmf behavior is
largely non-linear and thus cannot be accurately described using the existing
empirical and physical approaches. As a result, multi-layer perceptron artificial neural networks have been extensively
used to generate two highly accurate, a purely dimensional and a dimensionless,
empirical correlations describing the ULmf,. Using cross-correlation analyses, two unsuspected
effects, namely the wall effect ratio and the liquid surface tension, have been
unveiled and then incorporated as correlating variables in the neural network
correlations. The resulting mean relative error produced by the dimensional
correlation is about 16% while the estimated error associated with the
dimensionless-based correlation is 30%. The prediction errors from both
correlations are found to be insensitive to column-to-particle diameter
ratio. Moreover, the neural network approach has been shown to predict
with moderate success the minimum fluidization gas velocity, UGmf, in liquid-buoyed gas-activated three-phase fluidized
beds containing coarse particles (dv > 1 mm) at
high input gas fractions.
Keywords : Three-phase fluidization, minimum fluidization
velocity, neural network correlation, database
You
can get the ulmf.zip file to compute (Excel Worksheet)
the minimum liquid velocity.
You can get also the simflui3p.zip file (version
28/06/2001) that contains an Excel worksheet simulator to compute phase
hold-ups and bubble wake model parameters.
The neural correlations were developped with the
software = NNFit