A framework for model-based design of parallel experiments in dynamic systems

Advanced model-based experiment design techniques are essential for 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 the multiple experiments are designed in a sequential manner. The concept of model-based design of parallel experiments design is presented in this paper. A novel approach, viable for sequential, parallel and sequential-parallel design is proposed and evaluated through an illustrative case study.