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

Discrete Element Simulations and Machine Learning for Improving the Performance of Dry Catalyst Continuous Impregnation Processes