Optimal dynamic experiments for bioreactor model discrimination.

Abstract

This paper describes a general approach for dynamic model discrimination for continuous cultures and presents dynamic models for pure cultures of E. coli and C. utilis obtained using the method. For each pure culture system, four candidate models representing various levels of structure were considered. All models reduce to Monod growth kinetics at steady state. An optimized set of multivariable step inputs in selected manipulative variables was used to discriminate between candidate models. The models that best predicted the dynamic behavior were selected by comparison of model predictions with experimental data. Two discrimination functions were compared in terms of their ability to determine the optimal set of multivariable step inputs to discriminate between candidate models. Results indicate that model discrimination based on maximizing the minimum absolute difference between any two models for a given set of inputs possessed good potential for discrimination between candidate models. Models selected for E. coli and C. utilis from the model discrimination work are presented and compared with experimental data.