Design of Optimal Dynamical Experiments for Parameter Estimation

Measurement errors often lead to large uncertainties in parameter estimation for dynamical systems. However, a considerable improvement of the preciseness may be obtained by a suitable choice of the experimental conditions. An approach is given to calculate optimal input functions by solving a minimization problem, based on a calculation of the Fisher information matrix. This approach is applied to a model describing the growth of plant cells in suspension culture in a stirred tank reactor. The simple model is based on the metabolic structure, limiting rates, mass and energy balances of the cells as well as the reactor equations. A significant increase in the accuracy of the parameter estimations is shown to be possible by fed batch experiment design using the method indicated above, compared to the accuracy which could be obtained using batch experimental data.