Mechanistic models have become a very promising technique to describe chromatography processes. These models offer numerous advantages such as cost and time reduction and are applicable at any point of the product life cycle. Digital twins based on mechanistic models enable an in-depth understanding of even very complex separation problems.
However, the main drawback of mechanistic modeling is the cumbersome approach to model calibration. Multicomponent feed stocks lead to multidimensional parameter estimation problems with many unknown protein parameters. Standard model calibration techniques may result in unreasonable correlations and unrealistic physical parameter estimates. But how can we mitigate this risk, improve the model’s quality, and reduce the time we need for model calibration at the same time?
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