607202 Developing a Digital Twin for the L.B. Bohle Tablet Coating Process

Wednesday, November 18, 2020
Pharmaceutical Discovery, Development and Manufacturing Forum (26) (PreRecorded+)
Alfred Berchielli1, Ismail Anifowoshe2, Sandra Conway3, Pankaj Doshi1, Naomi Hodgins3, Reza Kamyar2, Peiyuan Liu1, Bruce C. MacDonald1, Zilong Wang2, Gopal Kasat4, Mothivel Mummudi4, Parth Sinha4 and Andreas Altmeyer5, (1)Worldwide Research and Development, Pfizer Inc., Groton, CT, (2)Pfizer Global Supply, Pfizer Inc., Peapack, NJ, (3)Pfizer Global Supply, Pfizer Inc., Newbridge, Ireland, (4)Tridiagonal Solutions Inc., San Antonio, TX, (5)L.B. Bohle, Ennigerloh, Germany

Tablet coating is a common unit operation in pharmaceutical manufacturing. Process engineers in the pharmaceutical industry are interested in optimizing the perforated pan coater operation to get low inter-tablet coating variability. In a tablet coater, tablet mixing, spraying and drying occur simultaneously. Tablets are mixed as a result of drum rotation and the action of helical baffles mounted inside the drum. The coating solution is sprayed on the tablet bed using spray nozzles. Hot air is directly supplied to ensure efficient drying of the coated tablets. Performing physical experiments with coaters to determine the optimal set of process parameters is, more often than not, impractical. Computer simulations incorporating the detailed physics of tablet coating offer a viable alternative to experiments.

In the current study, a coupled CFD-DEM model is developed to understand the impacts of coater hardware and process parameters on the inter-tablet coating variability in an L.B. Bohle tablet coater (Figure-1). Impact of operational parameters like drum rotation speed (Figure-2), fill level, tablet shape & size, spray rate, spray area (determined by spray gun-to-tablet distance and spray pattern air flow rates), is studied. The model was also used to compare the performance of the coaters from laboratory to industrial scales. The results from the simulation were used to understand which parameter plays a crucial role in determining the coating variability. The model results were also used to evolve guidelines for scale-up.


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