RT info:eu-repo/semantics/conferenceObject
T1 A Sum-Of-Squares Constrained Regression Approach for Process Modeling
A1 Pitarch Pérez, José Luis
A1 Sala, Antonio
A1 Prada Moraga, César de
K1 Constrained regression
K1 Process models
K1 Grey-box models
K1 SOS programming
AB Combining empirical relationships with a backbone of first-principle laws allow the modeler to transfer the available process knowledge into a model. In order to get such so-called grey-box models, data reconciliation methods and constrained regression algorithms are key to obtain reliable process models that will be used later for optimization. However, the existent approaches require solving a semi-infinite constrained regression nonlinear problem, which is usually done numerically by an iterative procedure alternating between a relaxed problem and an a posteriori check for constraint violation. This paper proposes an alternative one-stage efficient approach for polynomial regression models based in sum-of-squares (convex) programming. Moreover, it is shown how several desirable features on the regression model can be naturally enforced in this optimization framework. The effectiveness of the proposed approach is illustrated through an academic example provided in the related literature.
PB Elsevier
YR 2019
FD 2019
LK http://uvadoc.uva.es/handle/10324/36806
UL http://uvadoc.uva.es/handle/10324/36806
LA eng
NO 12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2019: Florianópolis, Brazil, 23–26 April 2019 Edited by Benoit Chachuat, Olivier Bernard, Julio E. Normey-Rico
NO Producción Científica
DS UVaDOC
RD 06-mar-2021