Model-Based Design of Parallel Experiments

Abstract

Advanced model-based experiment design techniques are essential for the rapid development, refinement, and statistical assessment of deterministic process models. One objective of experiment design is to devise experiments yielding the most informative data for use in the estimation of the model parameters. Current techniques assume that multiple experiments are designed in a sequential manner. However, multiple equipment can sometimes be available, and simultaneous (parallel) experiments could be advantageous in terms of time and resources utilization. The concept of model-based design of parallel experiments is presented in this paper. Furthermore, a novel criterion for optimal experiment design is proposed: the criterion aims at maximizing complementary information by considering different eigenvalues in the information matrix. The benefits of adopting such an approach are discussed through an illustrative case.