Paper Info
Title
Real-Time optimization of Uncertain Process Systems via Modifier Adaptation and Gaussian Processes
Abstract
In the context of static real-time optimization, the use of measurements allows dealing with uncertainty in the form of plant-model mismatch and disturbances. Modifier adaptation (MA) is a measurement-based scheme that uses first- order corrections to the model cost and constraint functions so as to achieve plant optimality upon convergence. However, first-order corrections rely crucially on the estimation of plant gradients, which typically requires costly plant experiments. The present paper proposes to implement real-time optimization via MA but use recursive Gaussian processes to represent the plant-model mismatch and estimate the plant gradients. This way, one can (i) attenuate the effect of measurement noise, and (ii) avoid plant-gradient estimation by means finite- difference schemes and, often, additional plant experiments. We use steady-state optimization data to build Gaussian-process regression functions. The efficiency of the proposed scheme is illustrated via a constrained variant of the Williams-Otto reactor problem.
Year
DOI
Venue
2018
10.23919/ECC.2018.8550397
2018 European Control Conference (ECC)
Keywords
Field
DocType
steady-state optimization data,Gaussian-process regression functions,uncertain process systems,modifier adaptation,MA,plant optimality,recursive Gaussian processes,measurement noise,plant-gradient estimation,finite- difference schemes,convergence,Williams-Otto reactor problem
Convergence (routing),Mathematical optimization,Regression,Computer science,Gaussian process,Constraint functions,Recursion
Conference
ISBN
Citations 
PageRank 
978-1-5386-5303-6
1
0.43
References 
Authors
3
5
Name
Order
Citations
PageRank
Tafarel de Avila Ferreira110.43
Harsh A. Shukla210.43
Timm Faulwasser319427.39
Colin Neil Jones466263.90
Dominique Bonvin513323.58