Optimal experiment design for dynamic bioprocesses: A multi-objective approach

• Published in: Chemical engineering science Vol. 78; pp. 82 - 97
• Main Authors: Telen, D., Logist, F., Van Derlinden, E., Tack, I., Van Impe, J.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 20-08-2012; Elsevier
• Subjects: Applied sciences; Biological and medical sciences; Bioprocess; bioreactors; Biotechnology; brewing; case studies; Chemical engineering; Criteria; Criterion selection; drugs; Dynamic optimisation; Dynamics; Exact sciences and technology; Experiment design; experimental design; Fishing; Fundamental and applied biological sciences. Psychology; labor; Mathematical models; Methods. Procedures. Technologies; Multi-objective optimisation; Optimal experiment design; Optimization; Others; Parameter estimation; Reactors; Scalars; Various methods and equipments; Parameter estimation; Dynamic optimisation; Criterion selection; Optimal experiment design; Bioprocess; Multi-objective optimisation; Modeling; Optimization; Design; Bioreactor; Batchwise; Production; Objective function
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2012.05.002

Dynamic process models can be used for operating, controlling and optimising important bioprocesses, e.g., pharmaceuticals production, enzyme production and brewing. After selecting an appropriate process model structure, parameter estimates have to be obtained based on real-life experiments. To reduce the amount of labour and often cost intensive experiments optimal experiment design (OED) is an indispensable tool. In optimal experiment design, dynamic input profiles have to be determined in order to obtain informative experiments. In the particular case of optimal experiment design for parameter estimation, a scalar measure of the Fisher Information Matrix is used as an objective function. Over the years, different criteria have been developed. However, the important question that remains is which criterion to choose. In this work, an approach to tackle the criterion selection is presented. In addition, a multi-objective optimisation approach is implemented, which enables to combine two, often competing optimisation criteria. The developed approach is illustrated with two case studies. The first case study is a fed-batch bioreactor model and the second case study is a Lotka Volterra fishing model.
► Generic strategy to select to most appropriate criterion in optimal experiment design (OED). ► Introduction of deterministic multi-objective optimisation techniques for OED. ► Evaluation method to study the existing trade-offs in design criteria. ► Illustration of the proposed strategy using two dynamic bioprocesses.