Batch Process Optimization with MATLAB
In pharmaceutical production, the design space is usually constructed from the data collected from many time-consuming and expensive experiments where parameters are varied one-at-a-time, while keeping others constant. This approach limits exploration of the design space and is unable to account for time varying operations that are often more optimal than those that are constant with time.
This presentation considers the alternative construction of the design space based on experiment data and a grey-box model of the reactions. The models are subsequently used to optimize the production process by changing the process variables. In addition, the effect of uncertainty and variability of the parameters on the process performance is also examined with a Monte-Carlo simulation.
For a 90-second overview of what you'll learn during this webinar, please watch Overview of Batch Process Optimization with MATLAB (1:32).
Highlights of this webinar include:
- How to reduce experiments required during the process characterization phase
- Improving the fundamental scientific understanding of a process by building a model
- Being able to optimize or scale-up the process by manipulating time-varying parameters
- Uncertainty quantification and analysis which is mandatory for Quality by Design (QbD)
About the Presenter
Paul Huxel, Ph.D., Senior Application Engineer, MathWorks Ireland
Paul holds a B.S./M.S. in Aerospace Engineering from The University of Colorado at Boulder and a Ph.D. in Aerospace Engineering from The University of Texas at Austin. Paul has over 8 years of aerospace industry experience, which includes extensive design, modeling, and analysis of complex GN&C systems for the US Navy and NASA using MATLAB and Simulink. After a 15-month sabbatical as a volunteer STEM teacher in rural Africa, Paul joined the Application Engineering Group at MathWorks in Galway, Ireland in August 2017. Since then, Paul's interests and skills have focused on image processing, computer vision, and machine/deep learning for the medical industry.
This event is part of a series of related topics. View the full list of events in this series.