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Adaptation strategies for tracking constraints under plant-model mismatch
Publikationstyp
Conference Paper
Publikationsdatum
2018
Sprache
English
Author
Enthalten in
Volume
51
Issue
18
Start Page
833
End Page
838
Citation
IFAC-PapersOnLine 51 (18): 833-838 (2018)
Publisher DOI
Scopus ID
Publisher
IFAC
Optimal operating conditions for a process plant are typically obtained via model-based optimization. However, due to modeling errors, the operating conditions found are often sub-optimal or, worse, they can violate critical process constraints. Hence, model corrections become a necessity and are done by exploiting measured process data. To this end, either model parameters are adapted and/or correction terms are added to the model-based optimization problem. The modifier-adaptation methodology does the latter by adding bias and gradient correction terms that are called modifiers. The role of modifiers and model parameters are often seen as competing, and which one of the two is better suited to track the optimality conditions is an open problem. This paper attempts to shed light on finding a synergy between the model parameters and the modifiers in the case when tracking constraints is sufficient for near-optimal performance. We demonstrate through the simulation study of a batch-to-batch optimization problem that a set of model parameters can be selected that mirror the role of modifiers. The modifiers are then added only when there is insufficient number of mirror parameters for independent constraint tracking.
Schlagworte
model-based optimization
model-plant mismatch
modifier adaptation
parameter estimation
parameter selection
real-time optimization
DDC Class
004: Computer Sciences
510: Mathematics