Prediction of liquid-solid wetting efficiency in trickle flow reactors

A comprehensive database of catalyst wetting efficiency measurements in tri ckle flow regime was gathered from 14 independent studies to conduct a thor ough evaluation of the performances of current correlations for the predict ion of wetting efficiency during vertical gas-liquid co-current down-flow i n randomly packed fixed bed reactors. Cross-examined with the database, sev eral shortcomings arising from a low level of accuracy or a lack in general ization revealed the weakness of existing estimation methods. An approach r elying on the combination of artificial neural network computing and dimens ional analysis (ANN-DA approach) helped to derive a highly accurate correla tion for wetting efficiency in trickle flow regime. This correlation yielde d an absolute average relative error of 8% and a standard deviation of 10 % . The five dimensionless groups intervening in the proposed correlation wer e a composite two-phase flow Reynolds, Re-g, liquid Stokes (St), Froude (Fr ) and Galileo (Ga) groups and a bed correction factor (S-b). (C) 2001 Elsev ier Science Ltd.