Optimal dynamic experiment design for estimation of microbial growth kinetics at sub-optimal temperatures: Modes of implementation

Application of optimal experiment design for parameter estimation enhances the informative character of experiments in view of maximising the parameter estimation quality. In this work, dynamic temperature inputs are applied to optimally estimate two kinetic parameters describing the temperature-dependence of the specific growth rate of micro-organisms. Validity of the model structure is constrained to (i) the sub-optimal growth temperature range, and to (ii) relatively small temperature gradients. This paper presents different ways to deal with these model validity constraints during optimal experiment design. Presented concepts can be valuable to other domains of (bio-)process modelling.