Digital design and industry 4.0 system for integrated, modular distributed pharmaceutical manufacturing networks
| Interdisciplinary Areas: | Data and Engineering Applications, Autonomous and Connected Systems, Future Manufacturing |
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Project Description
The aim of the project is to develop an innovative digital design and control system for distributed modular pharmaceutical manufacturing processes. The digital twin of the manufacturing process will be developed and used for digital design and an industry 4.0 system will be implemented with integrated process analytical technology (PAT) array and feedback control to demonstrate a robust manufacturing process. We aim to develop a first-principle understanding of protein crystallization dynamics under various bioprocessing conditions using population balance modeling. Employing physics-inspired neural networks (PINNs), we aim to elucidate the protein-solvent phase diagram, narrowing down the viable operating region for optimal crystal growth. An integrated crystallization-filtration-drying/dissolution flowsheet model will be implemented in PharmaPy, which is an open-source pharmaceutical simulation & digital design software package recently developed by the PIs within an FDA funded project. The digital twin will be used to investigate the robust design space and to implement a QbDD framework for a model mAb system. We will simulate end-to-end continuous (E2EC), end-to-end batch (E2EB) and design end-to-end optimal (E2EO) process using PharmaPy. Techno-economic analysis (TEA) and techno- sustainability analysis (TSA) will be performed to corroborate different manufacturing scales and routes.
Start Date
9/1/2025-12/1/2025
Post Doc Qualifications
Process control, process optimization, process systems engineering
Co-Advisors
Zoltan K Nagy, Chemical Engineering, zknagy@purdue.edu
Dave Thompson, Chemistry, davethom@purdue.edu
Bibliography
1. D. Casas-Orozco, D. Laky, V. Wang, M. Abdi, X. Feng, E. Wood, G.V. Reklaitis, Z.K. Nagy, Techno-economic analysis of dynamic, end-to-end optimal pharmaceutical campaign manufacturing using PharmaPy, AICHE J., 69 (9), e18142, 2023.
2. I. Hur, D.M. Casas-Orozco, D.J. Laky, F. Destro, Z.K. Nagy, Digital design of an integrated purification system for continuous pharmaceutical manufacturing, Chem. Eng. Sci, 285, 119534, 2024.