Projektbeschreibung
Erweiterte Nutzung des maschinellen Lernens in Sachen Energieeffizienz
Im Kampf gegen den Klimawandel ist es von entscheidender Bedeutung, dass wir den Ressourcen- und Energieverbrauch senken und gleichzeitig die Gesamteffizienz steigern. Das maschinelle Lernen erweist sich als ein vielversprechendes Instrument auf dem Weg zu diesem Ziel und konnte bereits in verschiedenen Sektoren und Industriezweigen zu entscheidenden Fortschritten bei der Automatisierung und Effizienz beitragen. Das Ziel des EU-finanzierten Projekts FUDIPO besteht darin, die Umsetzung von Praktiken des maschinellen Lernens in der gesamten europäischen Industrie auszuweiten, um die Energie- und Ressourceneffizienz drastisch zu steigern. Zu diesem Zweck werden im Rahmen des Projekts eine Optimierungsplattform, drei umfangreiche standortweite Systemdemonstratoren und zwei kleine Technologiedemonstratoren entwickelt. So können durch Datenerfassung und Simulationen erhebliche Verbesserungen realisiert werden.
Ziel
Machine learning have revolutionized the way we use computers and is a key technology in the analysis of large data sets. The FUDIPO project will integrate machine learning functions on a wide scale into several critical process industries, showcasing radical improvements in energy and resource efficiency and increasing the competitiveness of European industry. The project will develop three larger site-wide system demonstrators as well as two small-scale technology demonstrators. For this aim, FUDIPO brings together five end-user industries within the pulp and paper, refinery and power production sectors, one automation industry (LE), two research institutes and one university. A direct output is a set of tools for diagnostics, data reconciliation, and decision support, production planning and process optimization including model-based control. The approach is to construct physical process models, which then are continuously adapted using “good data” while “bad data” is used for fault diagnostics. After learning, classification of data can be automated. Further, statistical models are built from measurements with several new types of sensors combined with standard process sensors. Operators and process engineers are interacting with the system to both learn and to improve the system performance. There are three new sensors included (TOM, FOM and RF) and new functionality of one (NIR). The platform will have an open platform as the base functionality, as well as more advanced functions as add-ons. The base platform can be linked to major automation platforms and data bases. The model library also is used to evaluate impact of process modifications. By using well proven simulation models with new components and connect to the process optimization system developed we can get a good picture of the actual operations of the modified plant, and hereby get concurrent engineering – process design together with development of process automation.
Wissenschaftliches Gebiet
- engineering and technologymaterials engineeringfibers
- engineering and technologyenvironmental engineeringwater treatment processeswastewater treatment processes
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- social scienceseconomics and businesseconomicsproduction economics
- natural sciencesmathematicsapplied mathematicsstatistics and probability
Schlüsselbegriffe
Programm/Programme
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-SPIRE-2016
Finanzierungsplan
RIA - Research and Innovation actionKoordinator
722 20 VASTERAAS
Schweden