IMPROVING
THE PREDICTION OF LIQUID BACK-MIXING
IN
TRICKLE-BED REACTORS USING A NEURAL NETWORK
APPROACH
Simon Piché, Faïçal Larachi*, Ion
Iliuta, Bernard P.A. Grandjean
*Corresponding author - Phone: 1-418-656-3566; Fax:
1-418-656-5993; email flarachi@gch.ulaval.ca
Chemical Engineering Department; Laval University, Ste-Foy Québec,
Canada, G1K 7P4
J. Chem. Technol. Biotechnol., 77, 989-998 (2002)
Abstract
Current
correlations aimed at estimating the extent of liquid back-mixing, via an axial
dispersion coefficient, in trickle-bed reactors continue to draw doubts on
their ability to conveniently represent this important macroscopic parameter. A
comprehensive database containing 973 liquid axial dispersion coefficient
measurements (DAX) for trickle-bed operation reported in 22 publications
between 1958 and 2001 was thus used to assess the convenience of the few
available correlations. It was shown that none of the literature correlations
was efficient at providing satisfactory predictions of the liquid axial
dispersion coefficients. In response, artificial neural network modeling is
proposed to improve the broadness and accuracy in predicting the DAX, whether
the Piston-Dispersion (PD), Piston-Dispersion-Exchange (PDE) or PDE with
intra-particle diffusion model is employed to extract the DAX. A combination of
six dimensionless groups and a discrimination code input representing the
residence-time distribution models are used to predict the Bodenstein
number. The inputs are the liquid Reynolds, Galileo and Eötvos
numbers, the gas Galileo number, a wall factor and a mixed Reynolds number
involving the gas flow rate effect. The correlation yields an absolute average
error (AARE) of 22% for the whole database with a standard deviation on the
Key words:
trickle-bed, gas-liquid downflow, liquid back-mixing
database, neural network
Download our Excel worksheet to compute the liquid dispersion coefficients
You may download our Excel worksheet Trickle-bed simulator to simulate more on mass transfer, pressure
drop, liquid holdup and flow regime transition.
The neural correlations
were developped with the software NNFit