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Processes 2019, 7(3), 170; https://doi.org/10.3390/pr7030170

A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression

1
Systems Engineering and Automatic Control department, EII, Universidad de Valladolid, C/Real de Burgos s/n, 47011 Valladolid, Spain
2
Instituto Universitario de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain
3
Institute of Sustainable Processes (IPS), Universidad de Valladolid, C/Real de Burgos s/n, 47011 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Received: 26 February 2019 / Revised: 15 March 2019 / Accepted: 20 March 2019 / Published: 23 March 2019
(This article belongs to the Special Issue Process Modelling and Simulation)
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Abstract

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. View Full-Text
Keywords: grey-box model; machine learning; SOS programming; process modeling grey-box model; machine learning; SOS programming; process modeling
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Pitarch, J.L.; Sala, A.; de Prada, C. A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression. Processes 2019, 7, 170.

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