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At each iteration, the 'most informative' tendency model is used for designining the next dynamic experiment.
The optimization of batch chemical reactors is strongly affected by the accuracy of the Tendency model used.
This decomposition is beneficial since it allows the effective incorporation of the resulted tendency model into nonlinear model-based control algorithms.
A "tendency" model of free radical polymerization is used to re-investigate batch polymerization of styrene in solution in cyclohexane with slow and fast decomposing initiators.
The elucidation of the role of the initiator is a good example of the usefulness of the "tendency" model for studying complex polymerization mechanisms.
In this paper, the effect the process model mismatch, represented by the parametric uncertainty of the tendency model, has on the process optimization is examined.
Similar(49)
BHA dynamics and BHA tendency modeling tools are used occasionally.
The absence of a clear workflow has also hindered widespread use of the tendency models.
A run-to-run optimization strategy which integrates tendency models with Bayesian active learning is proposed.
A library of tendency models is used to increasingly bias bioreactor operating conditions towards an optimum.
Parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com