Mechanistic modeling requires many experiments: Myth or Fact?
Myth
A customer told us recently that mechanistic modeling required only 25% of the experimental work required for a classical statistical DoE approach and they also achieved much more, including deep process understanding.
The number of experiments required depends on the objectives such as the level of expected precision, the number of operating parameters that need to be predicted accurately, and the time and resources available. As a rule of thumb, 10-15 well targeted experiments are usually enough to get started for a chromatography modelling project and not all of them require fraction analysis.
In a publication earlier this year we showed that simplistic mathematical models derived from statistical analysis cannot describe the impact of pH and ionic strength observed in HIC processes whereas a mechanistic model can accurately describe experimental data and paves the way for numerical optimization of HIC processes. To learn more about the differences between mechanistic and statistical modelling, read our blog on this topic.
Download our infographic below to learn more about Mechanistic Modeling.
Curious about more myths and facts in the world of mechanistic modeling? Each post in our series tackles common misconceptions and provides insightful answers to frequently asked questions. Check out the other posts in this series:
- Mechanistic modeling can help secure scale-up: Myth or Fact?
- Mechanistic modeling is complex: Myth or Fact?
- Mechanistic modeling completely replaces experiments: Myth or Fact?
- Mechanistic modeling can help switch from batch to continuous: Myth or Fact?
Ypso-Facto is a service company helping industrial firms to develop, optimize and secure their chemical processes and bioprocesses.