451652 Computational Approaches for Modeling Fluid Bed Granulation Process
A multi-component modeling approach was explored that enables us to understand the fundamentals of fluid bed granulation process through both statistical modeling and first-principle modeling tools. A supervised machine learning algorithm that can simulate the evolution of process variables during fluid bed granulation process was explored, including: particle diameter, particle size distribution and loss on drying. Model predictions agree well with experimental analysis. Currently the focus is on a numerical framework that integrates design of experiment and the findings of statistical models with multi-scale mechanistic models such as discrete element model and computational fluid dynamics models. Using such models in early and late stage development phases will enhance the development timelines and increase the process product understanding.
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