A Multistage Stochastic Programming Approach in Real-Time Process Control
Please always quote using this URN: urn:nbn:de:0297-zib-6317
- Standard model predictive control for real-time operation of industrial production processes may be inefficient in the presence of substantial uncertainties. To avoid overly conservative disturbance corrections while ensuring safe operation, random influences should be taken into account explicitly. We propose a multistage stochastic programming approach within the model predictive control framework and apply it to a distillation process with a feed tank buffering external sources. A preliminary comparison to a probabilistic constraints approach is given and first computational results for the distillation process are presented.
Author: | Izaskun Garrido, Marc Steinbach |
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Document Type: | ZIB-Report |
Tag: | Multistage Stochastic Programming; Process Control; Real-Time Optimization |
MSC-Classification: | 90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING / 90Cxx Mathematical programming [See also 49Mxx, 65Kxx] / 90C06 Large-scale problems |
90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING / 90Cxx Mathematical programming [See also 49Mxx, 65Kxx] / 90C15 Stochastic programming | |
Date of first Publication: | 2001/03/26 |
Series (Serial Number): | ZIB-Report (01-05) |
ZIB-Reportnumber: | 01-05 |
Published in: | Appeared in: Online Optimization of Large Scale Systems. M. Grötschel, S. O. Krumke, J. Rambau (eds.) Springer, 2001, pp. 479-498 |