Data-Driven Modeling of a CO2 Refrigeration System

Glenn Andreasen, Jakob Stoustrup, Roozbeh Izadi-Zamanabadi, Ángel Á. Pardiñas, Armin Hafner

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

5 Citations (Scopus)

Abstract

This paper describes a data-driven method for system identification of a CO2 refrigeration system. Traditionally, the interaction between the measured variables is not utilized as they are highly dependent on the refrigeration system. In this work a data-driven method, namely subspace identification, is investigated for deriving a control-oriented model such that the dynamic interaction in the refrigeration systems can be utilized for e.g. fault detection and diagnosis. The subspace identification is applied on laboratory data obtained from a test setup located at NTNU in Trondheim, Norway. The obtained results offer promising perspectives for performance improvement in fault detection and diagnosis methods as well as control strategies.

Original languageEnglish
Title of host publication 2019 American Control Conference (ACC)
Number of pages6
PublisherIEEE
Publication date29 Aug 2019
Pages5385-5390
Article number8814871
ISBN (Print)978-1-5386-7901-2
ISBN (Electronic)978-1-5386-7926-5
DOIs
Publication statusPublished - 29 Aug 2019
Event2019 American Control Conference (ACC) - Philadelphia, United States
Duration: 10 Jul 201912 Jul 2019

Conference

Conference2019 American Control Conference (ACC)
Country/TerritoryUnited States
CityPhiladelphia
Period10/07/201912/07/2019
SeriesAmerican Control Conference
ISSN0743-1619

Fingerprint

Dive into the research topics of 'Data-Driven Modeling of a CO2 Refrigeration System'. Together they form a unique fingerprint.

Cite this