Identity delegation in policy based systems
Rajeev Gupta, Shourya Roy, et al.
ICAC 2006
This study focuses on the performance of large-scale nonlinear programming (NLP) solvers for the dynamic optimization in real-time of large processes. The MATLAB-based OptControlCentre (OCC) is coupled with large-scale optimization tools and developed for on-line, real-time dynamic optimization. To demonstrate these new developments, we consider the on-line, real-time dynamic optimization of the Tennessee Eastman (TE) challenge process in a nonlinear model predictive control (NMPC) framework. The example captures the behavior of a typical industrial process and consists of a two phase reactor, where an exothermic reaction occurs, along with a flash, a stripper, a compressor and a mixer. The process is nonlinear and open loop unstable; without control it reaches shutdown limits within an hour, even for very small disturbances. The system is represented through a first principles model with about 200 differential algebraic equations (DAEs). As a result, the NMPC formulation of this system presents some interesting features for dynamic optimization approaches. This study compares two state-of-the-art NLP solvers, SNOPT and IPOPT, for dynamic optimization on a number of challenging control scenarios, and illustrates some of the advantages of IPOPT for dynamic optimization. © 2003 Elsevier Science Ltd. All rights reserved.
Rajeev Gupta, Shourya Roy, et al.
ICAC 2006
Anupam Gupta, Viswanath Nagarajan, et al.
Operations Research
Maciel Zortea, Miguel Paredes, et al.
IGARSS 2021
Rafae Bhatti, Elisa Bertino, et al.
Communications of the ACM