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Rather than decoupling model identification and process optimization, we use information from process optimization to design optimal experiments for improving the quality of the kinetic model given the intended use of the model.
As compared to estimates from non-optimized dynamic experiments, more reliable CTMI parameter values were obtained from the optimal experiments within [15 °C, 45 °C].
The latter could only be estimated accurately from the optimal experiments within [15 °C, 45 °C].
Designing optimal experiments based on current or prior process knowledge is still an open research problem.
It is demonstrated how the Fisher-information matrix can be used as a generic tool for designing optimal experiments.
Subject of this paper is the design of optimal experiments for chemical processes described by nonlinear DAE models.
Similar(31)
A dynamic-real time optimization approach was developed for simultaneous on-line optimal experiment design and unknown parameters estimation.
The optimal experiment design is then proposed as a bi-level optimization problem.
It is a statistical technique for quickly optimizing performance of systems, with two general issues, first, designing an optimal experiment and second, analyzing its results [24, 28].
Robust optimal experiment design for dynamic system identification is cast as a minmax optimization problem, which is infinite-dimensional.
This has implications for optimal experiment design.
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