(116g) Development of a Mechanistic Model for Fluid Bed Granulation

Development of a mechanistic model for fluid bed granulation

An abstract for the AIChE Annual Meeting, Salt Lake City, November 2015

Co-authors, RCPE:  M. Askarishahi, M. Ebrahimi, P. Rajniak*, M. Zadravec

Co-authors, BI: M. Braun, A. Fetscher, M. Maus, R. Schmidtke, D. Steigmiller

Co-authors, PSE: S. Bermingham, D. Slade

Co-authors, IPPT: J. Khinast, S. Radl

RCPE = Research Center Pharmaceutical Engineering, Graz, Austria

BI = Boehringher-Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany

PSE = Process Systems Enterprise, Ltd., London, UK

IPPT = Institute for Process and Particle Engineering, Graz University of Technology, Austria

* Corresponding author, pavol.rajniak@rcpe.at

This presentation summarizes ongoing activities of a collaboration project of 4 partnering organizations (see above the list of co-authors and partners). The aim of the project is to systematically investigate, develop, implement and validate elements for application of the fluidised bed granulation (FBG) process model. The project thus considers the FBG model itself, implementation into gSOLIDS software, as well as the addition of formulation-, process- and equipment-specific model parameters required with actual industrial FBG process simulations.

Despite of being a widely-used unit operation, the application of (FBG) is still to some extent guided by empirical methods rather than by scientifically-based strategies. The development of realistic mathematical models that are combined with suitable process measurements and evaluation can yield powerful tools for knowledge-based control of process and product quality.

The complex interplay of various phenomena that govern the process dynamics of FBG at different scales poses a significant challenge in developing such models. Most importantly, a realistic FBG model has to incorporate phenomena associated with:

A/ Hydrodynamic modeling of the multi-phase mixture flow

B/ Heat and mass balances: Impact of process conditions on the granule moisture and growth

C/ Modeling of contact mechanics and granule formation

D/ Population balancing (PB) of agglomeration and breakage of different multi-component granules

It is understood that even in a highly detailed, mechanistic FBG model, some model parameters must be calibrated based on experimental data from a process. Clearly, carefully designed and executed experiments are important and necessary to support modelling activities. In summary, our contribution aiming on mechanistic modelling of FBG will discuss the following topics:

1. Experimental studies of the FBG process for different formulations at different scales (BI)
2. A novel experimental study of the temperature and humidity profiles in a laboratory granulator (BI)
3. CFD and coupled CFD-DEM simulations of the 2 –phase (gas-solid) isothermal granular flow in different granulators  (RCPE, IPPT)
4. CFD simulations of the 3 – phase (gas – solid – droplets) flow and evaporation in a laboratory scale granulator (RCPE)
5. Combination of the CFD models with population balancing (PB) in a laboratory scale granulator (RCPE)
6. Development of a simple (ideal mixer approach) macroscopic heat and mass balances model (HMBM), its calibration by fitting to the BI experimental data, testing of different evaporation rate expressions to predict the particle moisture at different process conditions (RCPE)
7. Development of a simple (ideal mixer approach) population balance model (PBM), fitting to the BI experimental data, testing of different agglomeration kernels (RCPE)
8. Combination of the above HMBM and PBM to predict granule growth at different process conditions (RCPE)
9. Comparison and combination of the RCPE models with existing gSOLIDS models and development of an optimal macroscopic model within the gSOLIDS environment (RCPE, PSE)

Partnering organizations realize that developing a detailed (CFD based) model for the whole granulation process is nowadays still a too ambitious goal. So the key strategy is to use information from such detailed models for development of more practical macroscopic models. For example, information about the flow patterns and temperature and moisture profiles (both, experimental and theoretical) should help in definition of ‘active zones’ or ‘drying zones’ (in which the agglomeration or drying and/or breakage take place) of granulators and their corresponding models.