  In this work, discrete element method (DEM) simulations coupled with machine
learning are used to study the process of dry impregnation. Our results show
that the particle bed contains two regimes. Regime 1 shows smaller inclination
angles and a larger mass hold-up which implies more forces restricting the
particle movement. Regime 2 reveals larger inclination angles and rotational
speeds and a smaller mass holdup, which indicates a smaller bed height. Using
Machine learning, we found a general function for the Relative Standard
Deviation (RSD) as a function of time, angle of inclination and speed of
rotation for both even and uneven flow rates for a full range of the parameters
fed in the LASSO algorithm. Machine learning gives insight on both regimes and
reveals that for low RPM and low angles, uneven spraying gives a lower RSD
which is consistent with our observations of the DEM studies.
