(206a) Impregnation of Catalyst: Batch and Continuum Processes | AIChE

(206a) Impregnation of Catalyst: Batch and Continuum Processes


Tomassone, M. - Presenter, Rutgers University
Borghard, W., Rutgers University
This project focuses on developing science-based methods for designing, and optimizing the catalyst impregnation process in batch and continuous equipment. Discrete element method (DEM) simulations are combined with parametrically and geometrically identical experiments to validate simulations. The DEM model is coupled with a novel algorithm that allows the transfer of fluid (water with metal), viscous and non- viscous, to and between particles. In previous meetings, the simulation with the algorithm has been compared and validated with matching experiments. Good agreement has been demonstrated in the results for the relative standard deviation (RSD) of mixing and liquid distribution for both double cone blender and cylindrical vessels. Using the validated DEM model, we have also investigated the effects of rotation speed, spray pattern, support size & morphology, and addition of baffles on the overall mixing. We investigated the effect of baffles and elucidate the relevant factors that control a continuous impregnation process. The two main objectives for this work are: 1) obtain fundamental understanding of continuous impregnation process for particles and powders and 2) investigate process parameters and develop scientific relationships to increase the efficiency of continuous impregnation. Some key parameters for the continuous impregnation process include the residence time distribution, the axial dispersion coefficient, the product RSD and the mass transfer coefficients. We have done a systematic study covering before and after steady state was reached focusing on the RSD and water content of tracers particles in the collection box. The focus was to reveal details about the parameters that affect the homogeneity of the particle bed, such as the mean residence time (MRT) and the residence time distribution (RTD) of the tracer particles, the separation of the nozzles and the flow rate distribution in the nozzles. We studied 3 different RPMs (1, 3 and 5), and 3 different angles (1, 3 and 5) in two different flow rate configurations (even and uneven) with 4 nozzles. We observe very interesting results. Our work shows that the bed appears to contain two regimes: Towards the beginning of the vessel: neighboring particles tend to remain close together (Regime 1). As tracer particles move through vessel, they tend to mix. Towards the end of the vessel, particles that were close together in the beginning of the vessel are now mixed (Regime 2). The two Regimes in the vessel should be treated differently.More water should be sprayed in Regime 1, where neighboring particles remain close together. Particles will be exposed to similar amounts of water and have more time to exchange fluid. Less water should be sprayed in Regime 2: water content uniformity will be benefited by water exchange between particles. We obtained better results when 70% of the total amount of fluid is sprayed in Regime 1 and 30% in Regime 2.

In summary, we developed a systematic approach to study the continuous impregnation process. We are now in a good position to test and optimize the effect of various parameters, the number of nozzles, the nozzle spacing, the wetted area, the drum speed, the Q patterns, etc. During startup, the number of particles inside the cylinder first increases and then reaches a steady state value. The higher the rotational speed, the sooner the steady state is reached and the shallower the bed is. In our studies, we also found that the RSD is proportional to a newly proposed index H, which trends well with RSD for the water content in the collection box and for the tracers after reaching SS, for either even or uneven patterns of flow rates. In all our studies, we see that the effects of inclination angle and rotational speed are consistent with Sullivan’s prediction. Additional work is needed to ensure that the continuous process can produce product of a quality equal to or better than the batch process.