Multi-scale modeling of fluid bed granulation

Aligned with the FDA and pharmaceutical industry's efforts to implement the quality by design paradigm and incorporate design-based principles to both new and existing manufacturing processes, this work aims to move towards a predictive model-based analysis to establish a process design space linking critical process parameters and formulation properties to critical quality attributes. A novel framework is presented that couples population balance modeling (PBM), discrete element methods (DEM) and computational fluid dynamics (CFD) to capture the granulation dynamics within a fluidized bed system.

The framework relies on coupled CFD–DEM simulations performed using  STAR-CCM+® to provide particle-level mechanistic information such as flux data, collision frequencies, relative collision velocities, residence time and drag forces. STAR-CCM+ provides two-way coupling model to couple Eulerian (fluid) and Lagrangian (particles) phases. As the pre-granulation blend consists of very fine particles (D50 of 80 µm), to reduce the computational load, the coarse grain model in STAR-CCM+ is used which reduces the number of particles by representing a group of small identical particles with a larger ‘parcel’ without losing the accuracy in calculating particle-particle and particle-fluid interactions. Passive scalar model is also used for Lagrangian phase to predict the residence time of the particles within the spray zone. A PBM is then used for describing macroscopic behaviors using CFD-DEM data to simulate the changes in the particle size distribution due to rate processes including aggregation, breakage, liquid addition and consolidation.

The framework demonstrates a comprehensive process model development by efficiently coupling multi-scale simulation techniques which can be used for effective process design, development and scale-up purposes.

Rutgers University
Rohit Ramachandran
Session Time Slot(s): 
06/03/2017 - 5:05pm-06/03/2017 - 5:30pm
Room I
Presentation Image: 
Session Track: 

Session Tracks (STAR Global Conference 2017)