Model-based QbC Strategy for Agglomeration Control of a Pharmaceutical Crystallization Process
Optimizing Industrial Crystallization
Program Overview
- Hybrid population balance model (PBM) to optimize temperature profiles and reduce agglomeration during crystallization of Compound K
- Thermocycles to enhance crystal growth and reduce agglomeration, with diminishing returns beyond a certain cycle count
- Show model-based approach that outperforms traditional methods, offering a robust framework for crystallization process optimization
This study presents a model-based approach using a hybrid population balance model (PBM) to optimize temperature profiles for minimizing agglomeration and enhancing crystal growth. The PBM integrates key crystallization mechanisms—including nucleation, growth, dissolution, agglomeration, and deagglomeration—and is applied to the crystallization of an industrial active pharmaceutical ingredient (API), Compound K. Model parameters were estimated through model-based design of experiments (MBDoE) and further refined using additional thermocycling experiments.
In-silico DoE simulations demonstrate that the hybrid PBM outperforms conventional approaches in evaluating process performance under agglomeration-prone conditions. Results confirm that thermocycles effectively reduce agglomeration and promote bulk crystal formation, although their efficiency plateaus beyond a certain number of cycles. Additionally, crystallinity was found to correlate with the degree of agglomeration in this system.
Overall, this model-based strategy offers a more robust framework for agglomeration control than model-free quality-by-control (QbC) methods and preliminary linear cooling experiments, providing valuable insights for optimizing industrial crystallization processes.
Speakers
Yung-Shun Kang
Purdue University - Davidson School of Chemical Engineering
Yung-Shun Kang is a Ph.D. candidate in Chemical Engineering at Purdue University, working in the Crystallization and Particle Technology Systems Engineering Lab under Dr. Zoltan Nagy. His research focuses on pharmaceutical crystallization, process modeling, and digital design of thermocycling crystallization, in collaboration with Takeda Pharmaceuticals. Yung-Shun holds a Master's degree from Purdue and a Bachelor's from National Taiwan University, both in Chemical Engineering. He also brings industry experience from TSMC, where he optimized thin-film processes in semiconductor manufacturing. His work bridges data-driven process design, advanced modeling, and experimental validation to support innovation in both pharma and tech sectors.
