Robust dynamic real-time optimization under parametric uncertainty for plant-wide processes

In this study, a two-level scheme with robust dynamic real-time optimization and distributed model predictive control (DMPC) is presented for plant-wide processes. On the top layer, chance-constrained programming (CCP) is employed in the real-time optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. On the lower layer, the on-line optimization of the whole system is decomposed into that of several small cooperative subsystems in distributed structures. The walking beam reheating furnace optimization and control problems are illustrated to verify the effectiveness and practicality of the proposed scheme.