Research abstract:
Crystallization is a vital purification and particle design step in pharmaceutical manufacturing, directly impacting yield, crystal size distribution (CSD), polymorphism, and downstream processability. To meet evolving industrial demands for robust, scalable crystallization processes, this research integrates advanced modeling strategies into a comprehensive digital design framework. Specifically, we developed a hybrid compartmental–population balance modeling (PBM) approach [1] to enable efficient scale-up of combined cooling–antisolvent crystallization systems.
This work features a deterministic PBM platform enhanced by iterative model-based experimental design (IMED) and high-resolution finite volume methods (HRFVM) to accurately estimate kinetic parameters and quantify uncertainty [2]. Key process parameters—such as seed loading, cooling trajectory, and antisolvent addition—were optimized through chance-constrained programming and validated across lab and pilot scales. Mixing effects were captured using compartment models informed by CFD-derived energy dissipation, enabling predictive CSD modeling under scale-up conditions.
The research also pioneers a model-free Quality-by-Control (QbC) framework to complement mechanistic modeling [3,4], enabling data-driven strategy selection and recipe development. By bridging experimental data with physics-informed models, this digital design methodology facilitates the creation of adaptive crystallization workflows with industrial scalability. Applications have been demonstrated in collaboration with Takeda Pharmaceuticals, showcasing the industrial readiness of the proposed digital twin framework.
References:
[1] Kang, Yung-Shun, Antonello Raponi, Almos Orosz, Guanghui Zhu, Neda Nazemifard, Charles Papageorgiou, and Zoltan Nagy. "Industrial Scale-up of Combined Cooling-Antisolvent Crystallization: A Compartmental-Population Balance Modeling Approach." In 2025 AIChE Annual Meeting. AIChE.
[2] Kilari, Hemalatha, Yash Barhate, Yung-Shun Kang, and Zoltan K. Nagy. "A Systematic Framework for Iterative Model-Based Experimental Design of Batch and Continuous Crystallization Systems." In Computer Aided Chemical Engineering, vol. 52, pp. 1501-1506. Elsevier, 2023.
[3] Kang, Yung-Shun, Hemalatha Kilari, Neda Nazemifard, C. Benjamin Renner, Yihui Yang, Charles D. Papageorgiou, and Zoltan Nagy. "Optimization-Based Digital Design for Agglomeration Control of a Pharmaceutical Crystallization Process." In 2024 AIChE Annual Meeting. AIChE, 2024.
[4] Kang, Yung-Shun, Hemalatha Kilari, Neda Nazemifard, C. Benjamin Renner, Yihui Yang, Charles Papageorgiou, and Zoltan Nagy. "Generalized Workflow for Model-Free Quality-By-Control: Recipe Development and Its Implementation in Pharmaceutical Crystallization." In 2024 AIChE Annual Meeting. AIChE, 2024.