Iterative Design of Dynamic Experiments in Modeling for Optimization of Innovative Bioprocesses
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Mariano Cristaldi
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 the 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. The parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA) to pinpoint which parameters are keys for estimating the objective function. Results obtained after just a few iterations are very promising.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Process Modeling & Control: A Special Issue of Chemical Product & Process Modeling
- Determination of Kinetic and Stoichiometric Parameters of Pseudomonas putida F1 by Chemostat and In Situ Pulse Respirometry
- Exponential Observer Design for State Estimation in a Class of Anaerobic Sulfate Reducing Bioreactor
- From Reacting to Predicting Technologies: A Novel Performance Monitoring Technique Based on Detailed Dynamic Models
- Nonlinear Analysis of Heterogeneous Model for an Industrial Ammonia Reactor
- Iterative Design of Dynamic Experiments in Modeling for Optimization of Innovative Bioprocesses
- Dynamic Process and Accident Simulations as Tools to Prevent Industrial Accidents
- Modeling Disturbance Dynamics to Improve Controller Performance in Industrial Loops
- On the Design of an In-Line Control System for a Vial Freeze-Drying Process: The Role of Chamber Pressure
- A Takagi-Sugeno Fuzzy Dynamic Model of a Concentric-Tubes Heat Exchanger
- Design of Dynamics of a Recuperative Catalytic Combustor: Enhancement in Operation and Control
- Formulation and Spectral Reduction of the Dynamical Model of a Circulating Fluidized Bed Combustor
