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Model-based optimal design of experiments (M-bODE) is a crucial step in model parametrization since it encloses a framework that maximizes the amount of information extracted from a battery of lab experiments. We address the design of M-bODE for dynamic models considering a continuous representation of the design. We use Semidefinite Programming (SDP) to derive robust minmax formulations for nonlinear models, and extend the formulations to other criteria. The approaches are demonstrated for a CSTR where a two-step reaction occurs.
Model-based optimal design of experiments (M-bODE) is a crucial step in model parametrization since it encloses a framework
that maximizes the amount of information extracted from a battery of lab experiments.
We address the design of M-bODE for dynamic models considering a continuous representation of the design.
We use Semidefinite Programming (SDP) to derive robust minmax formulations for nonlinear models,
and extend the formulations to other criteria. The approaches are demonstrated for a CSTR where a two-step reaction occurs.