(551e) Integrating Dynamic Economic Optimization and Model Predictive Control for Optimal Operation of Nonlinear Process Systems

Ellis, M., UCLA
Heidarinejad, M., University of California, Los Angeles
Liu, J., University of California, Los Angeles

Economic optimization of chemical processes has traditionally been
addressed through real-time optimization (RTO). In general, an RTO system
carries out economic optimization and computes optimal process
operation steady-states using steady-state process models. These
steady-states are used
by the feedback control systems, typically designed via model predictive
control methods, that force
the process to operate on these steady-states. In recent years, numerous
for the development of the so-called “smart manufacturing paradigm” have
led to several
attempts to integrate MPC and economic optimization of chemical processes
to deal with variable demand,
energy prices, variable feedstock and product transitions. In previous
work [1], we developed a Lyapunov-based economic MPC (LEMPC) method capable
of optimizing closed-loop performance with respect to general economic
consideration for nonlinear systems.
In the present work, we propose a conceptual framework for integrating
dynamic economic optimization and MPC for optimal operation of nonlinear
process systems. First, we introduce the proposed integrated framework.
This optimal operation strategy consists of an LEMPC that preforms upper
layer dynamic economic optimization to compute optimal process operating
time-varying trajectories which are used by the lower-level control layer that may use conventional
MPC schemes or even classical control. Such a framework takes advantage of the
LEMPC ability to compute optimal process operation time-varying policies
using dynamic
process model instead of traditional steady-state models. Second, we prove
practical stability of the proposed integrated dynamic economic
optimization and control framework including
an explicit characterization of the stability region of the
closed-loop system.
Third, we demonstrate through extensive simulations using realistic chemical
process models that such an integrated control paradigm can both (1) achieve
stability and (2) perform economically better than traditionally RTO
systems using steady-state models.
1. Heidarinejad, M., J. Liu and P. D. Christofides, "Economic Model
Predictive Control of Nonlinear Process Systems Using Lyapunov
Techniques,'' AIChE J., 58, 855-870, 2012.
See more of this Session: Economics and Process Control

See more of this Group/Topical: Computing and Systems Technology Division