Multiphysics simulation in pharmaceutical manufacturing: Optimizing mixers and granulators with CFD and DEM
Simulation’s key roles in improving quality and efficiency
Mixing and granulation are two of the most significant unit processes involved in pharmaceutical manufacturing, and multiphysics simulation enables engineers to develop fundamental insights into both.
Simulations provide a window into the typically opaque processes occurring inside process equipment like reactors, mixers, granulators and dryers, allowing engineers to determine if key performance indicators are being met, how they can be improved, and to guide process scale-up.
In this webinar, engineers from Johnson & Johnson will discuss their use of multiphysics Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) to explore the efficiency of mixers and granulators used in their pharmaceutical manufacturing processes.
CFD and DEM: enabling technologies for maximizing process efficiency
Multiphysics CFD predicts the coupled behavior of fluid, gas and particulate flows including heat and mass transport, providing an enhanced understanding of several key processes involved in pharmaceutical manufacturing. DEM simulates the motion of many interacting particles and tracks them in a numerically efficient manner, modeling contact forces and energy transfer due to collision and heat transfer between particles, which are particularly important in the design and optimization of mixing and coating processes. These simulations help identify the important factors for equipment design and for determining optimal equipment operating conditions.
In this webinar, process modeling engineers John Perrigue and Tim Kline of Johnson & Johnson will provide an overview of their use of simulation and highlight two examples of coupled CFD and DEM: assessing coating uniformity in a mixer and exploring improvements to the blend time in a granulator.
What you will learn in this webinar:
This webinar will provide an overview of the toolset used by Johnson & Johnson and how Simcenter was used to model key processes taking place in commonly used pharmaceutical manufacturing equipment including:
- High-shear granulators
- Conical mixers
In addition, you’ll learn about the growing portfolio of Siemens simulation capabilities dedicated to the pharmaceutical industry (including the only simulation solution that includes native CFD + DEM capabilities).
Presenters:
Senior Director, Digital Process Design, Johnson & Johnson Inc.
John joined Johnson & Johnson in 2004 within the pharmaceutical business sector. He has since worked in multiple roles and business sectors since joining J&J and has for the last 5 years been working on strategy and delivery of digital technologies and processes to support Industry 4.0 initiatives. John has over 32 years of experience in engineering and operations. He holds a BSc in Mechanical Engineering from University of Missouri-Rolla an MBA from Villanova University, and is a certified J&J Master Black Belt.
Process Modeling Engineer, Johnson & Johnson Inc.
Tim joined Johnson & Johnson in 2018 as a Process Modeling Engineer to support and shape modeling and simulation for process and production within the Consumer, Pharmaceutical and Medical Device businesses. He holds a BSc and MSc in Mechanical Engineering from Villanova University. Tim is subject matter expert within Discrete Element and Computational Fluid Modeling and has, most recently, been supporting the corporate effort to establish validation and verification best practices across the organization.
Portfolio Development Executive, Siemens Digital Industries Software
After getting a BSc in Chemical Engineering, Glenn has worked for over 35 years in the Computer Aided Engineering (CAE) space helping companies implement and gain value from building digital twins of their products and/or processes. At Siemens Digital Industries Software Glenn works in a business development role predominantly in the process based industries. The role is to understand a company's business needs and find solutions to their product or process design challenges through physics-based simulation.