(189d) Product Dynamic Transitions Using a Derivative-Free Optimization  Trust-Region Approach

Traditionally the optimization of processing systems has relied on the availability of an explicit
model together with the corresponding gradient information. However, there are some practical scenarios such as
(a) non-differentiable systems, (b) physical experimental systems, (c) simulation environments and (d) reduced order
systems where such a model and its gradient are not available. Under these scenarios the deployment of derivative-free
optimization strategies provide an alternative manner to cope with the optimization of such systems. In particular, in this
work we deploy a derivative-free optimization trust region approach to deal with the product dynamic optimization 
problem of processing systems. To this aim, we use a closed-loop model predictive control strategy where the system to be optimized is embedded
in a black-box dynamic simulation environment. The results demonstrate that black-box dynamic models can be
dynamically optimized assuming that the number of decision variables is not large. The first-principles dynamic model of
a binary distillation column embedded in the ASPEN dynamic simulation environment was deployed as our black-box
dynamic model, to demonstrate the advantages of solving product dynamic transition problems when an explicit model
of the dynamic model and/or its gradient information are not available.