Stochastic NMPC/DRTO of batch operations: Batch-to-batch dynamic identification of the optimal description of model uncertainty

Abstract

The effectiveness of stochastic online process optimization strongly depends on the choice of the uncertain parameters, which are used to characterize the uncertainty embedded in the process model. This contribution presents a framework for rapid identification of the optimal set of uncertain parameters, needed for the formulation of stochastic online optimization problems. This algorithm relies on a combination of approximate statistical analysis, multi-point/global sensitivity analysis and ad-hoc ranking indices, and is tailored for applications in the field of stochastic dynamic optimization/optimal control of campaigns of batch cycles. To demonstrate the potential of the proposed approach, we apply it within the optimization of a batch campaign, in the presence of equipment fouling and of dynamic variations in the campaign targets. The process model, utilized in all of these studies, is a batch adaptation of the Tennessee Eastman Challenge problem.