RT info:eu-repo/semantics/article
T1 A systematic grey-box modeling methodology via data reconciliation and SOS constrained regression
A1 Pitarch Pérez, José Luis
A1 Sala, Antonio
A1 Prada Moraga, César de
K1 Machine learning
K1 Aprendizaje automático
K1 Process modeling
K1 Modelado de procesos
AB Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the proposed methodology are illustrated through: (1) an academic example and (2) an industrial evaporation plant with real experimental data.
PB MDPI
SN 2227-9717
YR 2019
FD 2019
LK https://uvadoc.uva.es/handle/10324/56016
UL https://uvadoc.uva.es/handle/10324/56016
LA eng
NO Processes, 2019, vol. 7, n. 3, 170
NO Producción Científica
DS UVaDOC
RD 27-nov-2024