Heat and Mass Transfer in a Pharmaceutical Coating Process

The Wurster process is commonly used to modify the release rate or the taste of granulated beads in the pharmaceutical industry. Hereby, beads are coated with one or multiple functional films that control the release of drug substances or mask a specific taste. The coating process takes place in the Wurster tube, in which the particles are transported centrally upwards followed by a subsequent downwards movement outside the tube. Thus, the particle flow resembles a torus-shaped recirculation pattern.

From former studies, it is known that small droplets, a diluted coating solution and low spray rate improve the coating layer homogeneity¹. On the other hand, such a process is energy and time consuming. Low spray rates and small droplets promote spray drying (premature droplet drying, inhibits spray deposition on particles), reducing the process efficiency. Additionally, the coating quality increases with higher spray rates^1,2. From product quality point of view, the spray rate is capped by the amount of spray drying on the lower boundary and by overwetting, which would lead to agglomerations, on the upper boundary. Therefore, investigating the heat and mass transfer inside the coating device can help to find optimal process parameters.

In our work, a fully integrated CFD-DEM environment is used to model the transport phenomena in a lab-scale Wurster coater. The commercial DEM code XPS is coupled to AVL-FIRE for the CFD simulation. State-of-the-art models for momentum, heat and mass transfer between continuous and discrete phase are integrated in a fully coupled manner³. Temperatures, vapor mass fractions and evaporation rates of a coating process with a multicomponent spray solution are calculated. The amount of spray drying is quantified and related to the input parameters of the process.

1. van Kampen A, Kohlus R. Statistical modelling of coating layer thickness distributions: Influence of overspray on coating quality. Powder Technol. 2018;325:557-567. doi:10.1016/j.powtec.2017.11.031
2. van Kampen A, Kohlus R. Systematic process optimisation of fluid bed coating. Powder Technol. 2017;305:426-432. doi:10.1016/j.powtec.2016.10.007
3. Forgber T, Toson P, Madlmeir S, Kureck H, Khinast JG, Jajcevic D. Extended validation and verification of XPS/AVL-Fire^TM, a computational CFD-DEM software platform. Powder Technol. 2020;361:880-893. doi:10.1016/J.POWTEC.2019.11.008