Dynamic Real-time Optimization: From Off-line Numerical Solution to Measurement-based Implementation
| Authors: | Kadam Jitendra, Lehrstuhl fuer Prozesstechnik, RWTH Aachen University, Germany
Schlegel Martin, Lehrstuhl fuer Prozesstechnik, RWTH Aachen University, Germany
Srinivasan Bala, Laboratoire d'Automatique, EPFL, Lausanne, Switzerland
Bonvin Dominique, Laboratoire d'Automatique, EPFL, Lausanne, Switzerland
Marquardt Wolfgang, Lehrstuhl fuer Prozesstechnik, RWTH Aachen University, Germany |
|---|
| Topic: | 6.1 Chemical Process Control |
|---|
| Session: | Process Optimization and Predictive Control |
|---|
| Keywords: | Dynamic real-time optimization, hybrid control, measurements, NCOtracking, numerical methods, solution structure, uncertainty |
|---|
Abstract
The problem of optimizing a dynamic system in the presence of uncertainty is typically tackled using measurements. The methods widely used in the literature are based on repetitive optimization of a process model. Recently, tracking of the Necessary Conditions of Optimality (NCO tracking) has been proposed as a computationally less expensive alternative, which is based on the adaptation of a solution model using measurements. So far, the solution model, which contains information on the structure of the input profiles and the set of active constraints, has been derived manually based on physical insight and intuition. In this paper, based on recent results on the numerical optimization of dynamic systems, we present a systematic and automated approach to generate a solution model, towards an entirely automated procedure for dynamic optimization under uncertainty via NCO tracking.
