(269f) Optimal Experimental Design Under Uncertainty

In this work, especial attention is paid to the explicit modeling of all laboratory steps so as to prepare, implement, and analyze experiments in order to have a realistic definition of the numeric design problem and to formally include experimental restrictions and sources of uncertainties in the problem formulation. Moreover, whereas the effect of erroneous assumptions in the initially assumed parameter values have been covered in previous works, in this contribution, uncertainties are considered in a more general way including those which arise during an imprecise implementation of optimal planned experiments. In order to compensate for uncertainty influences, a feed-back based approach to optimal design is adopted based on the combination of the parallel and sequential design approaches. Uncertainty identification is done by solution of an augmented parameter estimation problem, where deviations in the experimental design are detected and estimated together with the parameter values. It has been shown that uncertainty influences vanish along with the iterative refinement of the experiment design variables and estimated parameter values.

Acknowledgment: The authors acknowledge the support from the Collaborative Research Center SFB/TR 63 InPROMPT ?Integrated Chemical Processes in Liquid Multiphase Systems? coordinated by the Berlin Institute of Technology and funded by the German Research Foundation.