PREDICTION OF LIQUID - SOLID
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)
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