Iterative Design of Dynamic Experiments in Modeling for Optimization of

MARIANO CRISTALDI; RICARDO GRAU; ERNESTO CARLOS MARTINEZ

Chemical Product and Process Modeling

Berkeley Electronic Press

Lugar: Berkeley, USA; Año: 2009 vol. 4 p. 1 - 26

1934-2659

Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. In this paper, a novel iterative methodology for model-based design of dynamic experiments in modeling for optimization is developed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 (rIL-11) whose DNA sequence has been cloned in an Escherichia coli strain. At each iteration, the proposed methodology resorts to a library of tendency models to increasingly bias bioreactor operating conditions towards an optimum. By selecting the ‘most informative’ tendency model in the sequel, the next dynamic experiment is defined by re-optimizing the input policy and calculating optimal sampling times. Model selection is based on minimizing an error measure which distinguishes between parametric and structural uncertainty to selectively bias data gathering towards improved operating conditions. Parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA) to pinpoint which parameters are key for estimating the objective function. Results obtained after just a few iterations are very promising.