Conference article

Designing models for online use with Modelica and FMI

Pål Kittilsen
Cybernetica AS, 7038 Trondheim/Statoil Research Centre, Trondheim, Norway

Svein Olav Hauger
Cybernetica AS, 7038 Trondheim, Norway

Stein O. Wasbø
Cybernetica AS, 7038 Trondheim, Norway

Download articlehttp://dx.doi.org/10.3384/ecp12076197

Published in: Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany

Linköping Electronic Conference Proceedings 76:19, s. 197-204

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Published: 2012-11-19

ISBN: 978-91-7519-826-2

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

Model-based online applications such as soft-sensing; fault detection or model predictive control require representative models. Basing models on physics has the advantage of naturally describing nonlinear processes and potentially describing a wide range of operating conditions. Implementing adaptivity is essential for online use to avoid model performance degradation over time and to compensate for model imperfection. Requirements for identifiability and observability; numerical robustness and computational speed place an upper limit on model complexity. These considerations motivate that models for online use should be balanced-complexity; physically based with online adaption possible.

Despite potential benefits; the effort required to implement balanced-complexity models; particularly at large scales; may deter their use. This paper presents techniques used in the design of balanced-complexity models. A Modelica-based approach is chosen to reduce implementation effort by interfacing exported Modelica models with application code by means of the generic interface FMI. The suggested approach is demonstrated by parameter estimation for a process of offshore oil production: a subsea well-manifold-pipeline production system.

Keywords

Modeling; process control; process models; process simulators; offshore oil and gas production; Modelica; subsea production; multiphase flow; balanced-complexity models; nonlinear model-predictive control; FMI

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