A gradient based strategy for integrating Real Time Optimizer (RTO) with Model Predictive Control (MPC)

TEODORO ALAMO; ANTONIO FERRAMOSCA; ALEJANDRO HERNAN GONZALEZ; DANIEL LIMON; DARCI ODLOAK

Conferencia; IFAC Conference on Nonlinear Model Predictive Control 2012 (NMPC'12); 2012

In the process industries it is often desirable that advanced controllers, such as modelpredictive controllers (MPC), control the plant ensuring stability and constraints satisfaction,while an economic criterion is minimized. Usually the economic objective is optimized by anupper level Real Time Optimizer (RTO) that passes steady state targets to a lower dynamiccontrol level. The drawback of this structure is that the RTO employs complex stationarynonlinear models to perform the optimization and has a sampling time larger than the controllerone. As a consequence, the economic setpoints calculated by the RTO may be inconsistent forthe dynamic layer. In this paper an MPC that explicitly integrates the RTO structure into thedynamic control layer is presented. To overcome the complexity of this one-layer formulation agradient-based approximation is proposed, which provides a low-computational-cost suboptimalsolution. It is shown that the iterative application of the proposed strategy derives in a controllaw that ensures convergence and recursive feasibility under any change of the economic function.The proposed strategy is tested in a simulation on a subsystem of a fluid catalytic cracking(FCC) unit.