How to Fit Time Course Experimental Data to a Model for Process Optimization and Scale-up
Using Data-Rich Experimentation for Process Optimization
Übersicht der Inhalte
- Guidelines on data-rich experimentation (DRE)
- Hardware for DRE
- Data analysis and model building demonstration (Reaction Lab)
This free online event is for chemists and engineers interested in understanding their processes at every scale to deliver increasingly complex processes under compressed timelines.
Topics include:
- Use of automation to increase the information density of every experiment
- Innovative problem-solving tools to avoid "trial and error experimentation," which carries a significant risk of failure
- The use of predictive process modeling that can be built quickly and allows users to remain focused on process goals
About the Presenter
Neil Hawbaker
Principal Consultant - Scale-up Suite
Neil Hawbaker is a technical application consultant with METTLER TOLEDO/Scale-up Suite. He earned his Ph.D. under the guidance of Dr. Donna Blackmond at Scripps Research Institute, with a focus on flow chemistry, reaction kinetics, and mechanistic understanding of catalytic reactions. Following his graduate work, he was a principal investigator at DEVCOM CBC, a government organization, and ran several projects formulating products for large scale remediation of toxic chemicals. Over the past two years, he has been part of the Scale-up Suite Team at METTLER TOLEDO AutoChem, and helps customers use their time course data to develop mechanistic models for process optimization and scale-up.
