Dynamic product transitions are essential for achieving high product
quality and reducing production costs. However, optimizing dynamic
product transitions is a challenging task due to the complex dynamics of
the process and the uncertainty in the measurements. In this work, a
data-driven Bayesian approach for optimal dynamic product transitions is
proposed. The approach is based on a dynamic optimization problem that
is solved using a Bayesian optimization algorithm. One of the advantages
of this approach for process optimization tasks is that it does not
require a first-principles dynamic mathematical model. The approach is
applied to three case studies. The results show that the proposed
approach finds optimal transition trajectories meeting product
composition requirements using smooth control actions. The approach is
also able to cope with noisy measurements, which is an important feature
in real-world applications. The proposed approach has several advantages
including being data-driven, able to cope with noisy measurements.