Abstract
Results are presented from the optimal operation of a fully automated robotic liquid handling
station where parallel experiments are performed for calibrating a kinetic fermentation model.
To increase the robustness against uncertainties and/or wrong assumptions about the parameter
values, an iterative calibration and experiment design approach is adopted. Its implementation
yields a stepwise reduction of parameter uncertainties together with an adaptive redesign
of reactor feeding strategies whenever new measurement information is available. The case
study considers the adaptive optimal design of 4 parallel fed-batch strategies implemented
in 8 mini-bioreactors. Details are given on the size and complexity of the problem and the
challenges related to calibration of over-parameterized models and scarce and non-informative
measurement data. It is shown how methods for parameter identifiability analysis and numerical
regularization can be used for monitoring the progress of the experimental campaigns in terms of
| Original language | English |
|---|---|
| Pages (from-to) | 765-770 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Publication status | Published - 2018 |
Research Field
- Efficiency in Industrial Processes and Systems
Keywords
- Parallel robotic liquid handling station
- E. coli kinetic model
- Optimal experimental design for model calibration
- Adaptive input design
- Identifiability and ill-conditioning analysis