(334d) Continuous Impregnation of Catalyst Particles in a Rotating Drum Using Discrete Element Method and Machine Learning Approaches.

The dry impregnation of catalysts is a crucial step in the preparation of heterogeneous catalyst supports, however there has not been a lot of work done computationally on this process. In this work, discrete element method (DEM) simulations coupled with machine learning are used to study the process of dry impregnation. We have previously developed a model, which has been further validated with geometrically equivalent experiments and the results show very good agreement.

Our results show that the particle bed appears to contain two regimes. The size of the Regimes is a function of inclination angle and RPM. In Regime 1 we observe smaller inclination angles and larger mass hold up implies more forces restricting the particle movement. In regime 2 we observe larger inclination angle and/or RPM and smaller mass holdup, which implies a smaller bed height and therefore a large Regime 2. Using Machine Learning we found a function for the Relative Standard Deviation as a function of time, the angle of inclination of the vessel and the speed of rotation, and for both even and uneven flow rates: We are able to predict the full Relative Standard Deviation (RSD) vs time for intermediate values of all rotational speeds and angles within the limits we input in the LASSO algorithm. We also found a qualitative estimation of both regimes 1 and 2. In general for uneven spraying we observe a faster decay of the RSD, which should give a better product quality. Machine learning also reveals that for low RPM and low angles uneven spraying RSD is lower (better product quality), which is consistent with our previous observations of the DEM studies.