israel ortiz

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) nondifferentiable 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 provides 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.