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FUDIPO

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Future Directions of Production Planning and Optimized Energy and Process Indus...

Future Directions of Production Planning and Optimized Energy and Process Industries 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 i... ver más

31/01/2021

MDU

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

MALARDALENS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Ver 12 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-01-31

SPIRE-02-2016: Plant-wide monitoring and control o...

I+D

Cerrada hace 9 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

12 Participantes

MDU

1.14M€ | Lider

Fraunhofer

433.52K€ | Participante

Bestwood

591.08K€ | Participante

MTT

303.38K€ | Participante

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Duración del proyecto: 54 meses Fecha Inicio: 2016-07-20
Fecha Fin: 2021-01-31

Líder del proyecto

MALARDALENS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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