Evaluation of Input Parameterization for Batch Process Optimization
For the optimization of dynamic systems, it is customary to use measurements to combat the effect of uncertainty. In this context, an approach that consists of tracking the necessary conditions of optimality is gaining in popularity. The approach relies strongly on the ability to formulate an appropriate solution model, i.e. an approximate parameterization of the optimal inputs with a precise link to the necessary conditions of optimality. Hence, the need to be able to assess the capability of a solution model to optimize an uncertain process. This paper introduces a loss function that can be used to verify the conjecture that the solution model derived from a simplified process model can be applied to a more rigorous process model with negligible loss in performance. This conjecture is tested in simulation via the dynamic optimization of a batch distillation column.