A Model Discrimination Approach for Data Analysis and Experimental Design

Abstract

A general model discrimination approach is presented that enables data based model structure discrimination as well as model discriminating experimental design. Results of closed-loop controlled steady-state fermentations with the methylotrophic yeast Candida boidinii are used to clearly discriminate the "right" model out of a group of 10 competing models (53% model probability). Using the identified model the kinetics of batch and fed-batch fermentations with Candida boidinii could be described too. The applicability of the model discriminating experimental design approach is shown by simulation results using the kinetics of Zymomonas mobilis.