Optimizing Drug Production with Hybrid Digital Twins
Problem:
Biopharm manufacturers are looking to optimize cell growth and drug production efficiency within bioreactors and production tanks. In drug manufacturing, maintaining an ideal growth environment for cells is complex, involving precise control over factors such as fluid mixing, mass transfer, and hydrodynamics. Variability in these parameters can negatively impact cell growth and, consequently, the quality and yield of drug products.
Solution:
This use case combines 3-D computational fluid dynamics (CFD), AI/ML techniques, and cell metabolic modeling. This provides a system process model that predicts the relationship between the hydrodynamics and growth environment for cells. The process model is then packaged using the Ansys digital twin solution toolkit for external deployment. It can be wrapped as a WebApp to be deployed to IoT or an edge stack.
The digital twin provides a real-time understanding of drug production inside the tank. This includes both the fluid mixing patterns and mass transfer and their impact on production. The solution implements computational fluid dynamics (CFD) to predict fluid flow patterns and gas distribution: The CFD model also predicts transport of oxygen from the gas phase to the liquid phase. A population balance approach models bubble break-up and coalescence.
Outcomes:
- Reduce unplanned down-time
- Maximize tank output
- Estimate design space and operating space
- Virtual sensors provide additional information not readily available
- Provide insights to non-expert operations specialists
Key Players:
The Ansys digital twin platform provides the foundation of this use case. Ansys develops and markets CAE/multiphysics engineering simulation software for product design, testing and operation worldwide.
Eli Lilly & Company is the biopharm manufacturer that specified the requirements for this use case. Lilly is a medicine company turning science into healing to make life better for people around the world.