Robust Implementation of Optimal Decisions Using a Two-Layer Chance-Constrained Approach

Abstract

In this work, the implementation of optimal and robust decisions in the presence of various uncertainties comprising model parameters, external conditions, and the closed loop behavior of basic controllers is presented. In order to compute the optimal and reliable decisions, a chance-constrained optimization problem is formulated. The efficient solution approach is based on the relaxation of the original stochastic optimization problem formulation to a standard nonlinear programming problem. By this means, nominal optimal solutions and operating points are regularly adapted in order to guarantee both feasibility and process operation as close as possible to the nominal optimum. The solution implies the minimization of additional costs, which come from conservative strategies that compensate for uncertainty. The experimental verification of the developed approach is carried out on a high-pressure distillation pilot plant for the separation of an azeotropic mixture.