SPEAKER:	Rui Huang Department of Chemical Engineering Carnegie Mellon University
TITLE:	Advanced Control and Dynamic Real-time Optimization for Large-scale Processes
DATE:	March 1 2011
TIME:	11:30 am
PLACE:	JHE 342

ABSTRACT

Current advances of optimization algorithm and software enable us to solve large-scale optimization problems online. This improves the economic performances of dynamic real-time optimization (D-RTO) and nonlinear model predictive control (NMPC), due to that more complicated models can be considered. This talk discusses my PhD work in this area and a large-scale air separation unit (ASU) is used as simulation examples. 

• We first derive a first-principle dynamic model for an industrial air separation unit. The recently developed advanced step method is used to solve both set-point tracking and D-RTO online. It shows that set-point tracking NMPC based on the first-principle model has superior performance against that with linear data-driven model. In addition, the D-RTO generates around 6% cost reduction compared to set-point tracking NMPC. Moreover the advanced step method reduces the online computational delay by two orders of magnitude.
• Then we deal with a realistic set-point tracking control scenario that requires achieving offset-free behavior in the presence of plant-model mismatch. In this case, a nonlinear state estimator is used to reconstruct the plant states from outputs. We propose two formulations and show that both approaches are offset-free at steady state. Moreover, robust stability for the closed-loop system is analyzed even though there is no general separation principle for nonlinear systems.
• Finally, we focus on the dynamic real-time optimization for cyclic processes. Two formulations with guaranteed nominal stability are proposed and they ensure that the system converges to the optimal cyclic steady state.