Prediction of liquid holdup is of significance in designing
and
in evaluating the performance of trickle bed contactors. The present
work focuses on the development of Gradient Boosting Machines (GBM)
for the prediction of total and dynamic liquid holdup in trickle bed
reactors. A comprehensive data set of 394 data points of total liquid
holdup and 416 data points of dynamic liquid holdup curated from open
literature is used in this study. We built GBM models with the input
data sets containing 11 governing variables. GBM provided excellent
predictions for both data sets. We have also compared the GBM predictions
with that of the Random Forest (RF) and Artificial Neural Networks
(ANN) predictions. As GBM provided the best performance, we further
employed SHAP (SHapley Additive exPlanations) with GBM black box models to get local and global
interpretability. Also, we have used SHAP to identify informative
subsets of governing variables. The work shall pave the way for use
of GBM in prediction of hydrodynamic parameters in multiphase systems.