PREDICTION OF LIQUID - SOLID
WETTING EFFICIENCY
IN
TRICKLE FLOW REACTORS
Faïçal Larachi, Lamia Belfares & Bernard P.A. Grandjean
Department
of Chemical Engineering & CERPIC, Laval University, Québec, Canada G1K 7P4
Int.
Comm. Heat & Mass Transfer, 28, 595-603 (2001)
ABSTRACT
A
comprehensive database of catalyst wetting efficiency measurements in trickle
flow regime was gathered from 14 independent studies to conduct a thorough evaluation
of the performances of current correlations for the prediction of wetting
efficiency during vertical gas-liquid co-current down-flow in randomly packed
fixed bed reactors. Cross-examined with the database, several shortcomings
arising from a low level of accuracy or a lack in generalization revealed the
weakness of existing estimation methods. An approach relying on the combination
of artificial neural network computing and dimensional analysis (ANN-DA
approach) helped to derive a highly accurate correlation for wetting efficiency
in trickle flow regime. This correlation yielded an absolute average relative
error of 8% and a standard deviation of 10 %. The five dimensionless groups
intervening in the proposed correlation were a composite two-phase flow
Reynolds (ReLG), liquid Stokes (StL), Froude (FrL) and Galileo (GaL)
groups and a bed correction factor (Sb).
You can get
the wetting.zip file to compute (Excel
Worksheet) wetting efficiency.
The neural correlation was developped with the
software NNFit