(208d) Population Balance Modeling of a Crystallization Process: Modeling and Optimization Strategies for Mechanism Identification

Crystallization processes of small organic molecules are an important purification step in the discovery and synthesis of new active pharmaceutical ingredients. Population balance modeling can help to reduce experimental efforts during the development and optimization of a robust manufacturing process, thus reducing the overall development cost.

In this work, we use gCrystal, a commercially available population balance modeling package, and Monte Carlo based approach, which was developed in-house. The g-crystal package is utilized to solve the 1D PBM. The Monte Carlo approach is utilized to expand the dimensional complexity of the model from one to two characteristic lengths describing the particle population. The majority of the experimental data which was used to estimate the underlying kinetics parameters of nucleation, growth, and agglomeration was obtained via in situ analytical tools. Details of the numerical solution of the in-house MC method and optimization methods utilized for mechanism identification are described in details.