WebCAST seminar on "Dynamic Real-Time Optimization: Concepts in Modeling,
Algorithms and Properties"
by Lorenz T. Biegler
Bayer Professor of Chemical Engineering at Carnegie Mellon University
Date: November 28, 2007, 10 am-12 pm (EST)
Dial-in from the comfort of your office to hear the presentation
Deadline to Register: November 23, 2007 (details at
The webcast develops and discusses nonlinear programming (NLP)
strategies for the optimization of nonlinear dynamic models that arise
in both off-line and on-line applications in chemical process
engineering. In particular, Dynamic Real-Time Optimization can play a
significant part in the decision-making hierarchy that includes
logistics, planning, scheduling and control. Its basic components deal
with estimation of the system and identification of a system model,
optimization of a system model and regulation to reject
disturbances. Moreover, the inclusion of a consistent set of nonlinear
process models is essential in order to coordinate optimization
decisions made at different levels in the hierarchy.
The webcast briefly presents and summarizes nonlinear programming
methods for dynamic optimization. In particular, it discusses
simultaneous NLP formulations along with large-scale NLP solvers for
dynamic optimization and demonstrates its effectiveness with
real-world examples. Also described is the extension of this approach
to nonlinear model predictive control (NMPC). In the last few years,
these have emerged as efficient and reliable on-line NMPC strategies.
Finally, the webcast discusses the integration of dynamic models for
off-line optimization to on-line model predictive control (MPC). In
particular, we will discuss a fast sensitivity-based nonlinear MPC
strategy that is not only consistent with rigorous off-line dynamic
optimization models but requires very little on-line computation. A
similar strategy will also be presented for moving horizon estimation
with nonlinear models. All of these concepts will be illustrated with
several case studies drawn from process engineering.
Professor Larry Biegler is the Bayer Professor of Chemical Engineering at
Carnegie Mellon University. He received a BS degree from Illinois Institute
of Technology and MS and PhD degrees from University of Wisconsin, Madison,
all in chemical engineering. Prof. Biegler's research projects are in the
areas of design research and systems analysis. His research centers on the
development and application of concepts in optimization theory, operations
research, and numerical methods for process design, analysis, and control.
He has received numerous honors and awards including the Presidential Young
Investigator Award from the National Science Foundation, the Curtis McGraw
Research Award from the American Society for Engineering Education, and the
Computing in Chemical Engineering Award from the CAST Division of the
American Institute of Chemical Engineers.