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Advances in Nonlinear Programming for Large-scale Dynamic Process

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Abstract:
As dynamic process models become more widely used, there is an increased demand for efficient optimization strategy for these systems. In particular, on-line applications for real-time dynamic optimization require strategies that are reliable and fast. This talk presents recent advances for large-scale nonlinear programming algorithms for dynamic optimization. The approach focuses on a simultaneous approach for dynamic optimization, where both the state and control variable profiles are discretized. This equation-based approach allows a transparent handling of inequality constraints and unstable systems. However, large scale ononlinear programming strategies are essential. For this purpose, a novel barrier method, called IPOPT, is described. This NLP algorithm incorporates a number of features for handling inequalities and improving global convergence through filter line search methods. The overall approach is Newton-based with analytic second derivatives and this leads to fast convergence rates. Computational results with up to two million variables will be presented. In addition, the IPOPT algorithm can be extended naturally to deal with complementarity constraints, which often arise and equilibrium and hybrid problems. Here theoretical modifications needed to handle these problems are described, a numerical study on complementary problems is presented and a case study for a hybrid dynamic system will be included. These results the synergy of incorporating advanced optimization tools with appropriate modeling strategies for dynamic and hybrid process systems.

http://dynopt.cheme.cmu.edu
Type of Seminar:
Public Seminar
Speaker:
Prof. Lorenz T. Biegler
L. T. Biegler, Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA
Date/Time:
Jun 05, 2002   17:15
Location:

ETH Zentrum, Gloriastrasse 35, Building ETZ, Room E6
Contact Person:

E. Bonanomi
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Biographical Sketch:
Lorenz T. (Larry) Biegler is currently the Bayer Professor of Chemical Engineering at Carnegie Mellon University, which he joined after receiving his PhD from the University of Wisconsin in 1981. His research interests are in the areas of computer aided process analysis and design and include flowsheet optimization, optimization of systems of differential and algebraic equations, reactor network synthesis and algorithms for constrained, nonlinear process control. Prof. Biegler has been a visiting scholar at Northwestern University, a scientist-in-residence at Argonne National Lab, a Distinguished Faculty Visitor at the University of Alberta and a Gambrinus Fellow at the University of Dortmund. He has authored or co-authored over 170 archival publications, authored or edited five books and presented numerous papers at national and international conferences. He is the recipient of the AIChE McAfee Award (Pittsburgh Section), the AIChE Computers in Chemical Engineering Award, the ASEE Curtis McGraw Research Award as well as a 1985 Presidential Young Investigator Award from the National Science Foundation. He has held several offices in the American Institute of Chemical Engineers and is also a member of SIAM, ACS and Sigma Xi. In addition, Professor Biegler has been an active consultant on process design and optimization strategies for the chemical and process industry.