Sparse nonlinear identification for control-oriented modeling of an organic Rankine cycle system

[en] Accurate low-order models are essential for control and optimization of thermodynamic energy systems, yet the nonlinear and time-varying behavior of Organic Rankine Cycle (ORC) units poses a challenge for standard linear identiﬁ cation. This paper presents a convex sparse identiﬁ cation framework for nonlinear system modeling, employing a set-membership formulation to obtain compact, interpretable models with guaranteed prediction bounds. The method automatically selects the most relevant polynomial interactions from large basis functions candidates, balancing accuracy and complexity without relying on noise statistics. Experimental validation on an 11 kWel ORC test bench demonstrates excellent prediction accuracy (FIT = 88.1%) with only 46 active basis functions, outperforming linear, piecewise-linear, and multiple-model Bayesian benchmarks. The identiﬁed model preserves physical interpretability through bilinear terms representing heat-ﬂow coupling, and its compact structure is suitable for real-time model predictive control implementation.

Disciplines :

Mechanical engineering

Author, co-author :

Hernandez Naranjo, Jairo Andres ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Systèmes énergétiques

Ruiz, Fredy ; Politecnico di Milano

Lemort, Vincent ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Thermodynamique appliquée

Language :

English

Title :

Sparse nonlinear identification for control-oriented modeling of an organic Rankine cycle system

Publication date :

25 January 2026

Journal title :

Control Engineering Practice

ISSN :

0967-0661

eISSN :

1873-6939

Publisher :

Elsevier BV

Volume :

169

Pages :

1-11

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S0967066126000481?httpAccept=text/xml

Available on ORBi :

since 30 January 2026

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