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 26, 2007 (details at 

http://www.castdiv.org/WebCAST.htm)



Abstract:

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.



Biographical Sketch:

Professor Lorenz 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.