(561a) Simulation of Drying of Pharmaceutical Wet Granules in a Fluidized Bed Dryer Using Coupled CFD-DEM Approach

Fluidized bed drying is a topic of interest for scientists and engineers because of its complicated physics and its presence in a wide range of industrial processes e.g. pharmaceutical product manufacturing, food processing, wood processing, reduction of iron ore, flue gas cleaning, the roasting of sulfide ores, drying of coal, catalyst industry and many more. Here, we propose a model based on coupled Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) approach to simulate the drying of granular particles in fluidized bed dryer associated to the drug manufacturing process. The drying process of wet granular particles in a fluidized bed dryer involves momentum, heat and mass transfer between the particles and the drying medium which is inherently a multiphase-multicomponent flow problem.

Methods:

The model was implemented using an opensource software CFDEM coupling where the fluid phase was solved by OpenFOAM codes and the motion of particles were calculated by LIGGGHTS codes. The information between the fluid phase and particles were exchanged at certain time intervals. The software CFDEM coupling was used because of its flexibility to include new models and its ability to handle large scale systems.

Results:

The model was validated with the results available in existing literatures. The CFDEM coupling was capable of capturing the physical fluidization phenomena and predicting the fluidization velocity correctly for a packed particle bed. Our model was capable of capturing the heat and momentum transfer between the particles and the drying medium. Present work is going on the modification of the model to include moisture transfer between the particles and drying medium. It was observed that the fluidization of particles significantly improved the performance of the fluidized bed dryer.

Conclusions:

This validated coupled CFD-DEM model provides a better qualitative and quantitative understanding of the effects of different process parameters on the drying process which can be beneficial for the industry. This model can further help set up reliable scale up, troubleshooting or optimization schemes by greatly replacing the burden of the cost of design of experiments.