Descripción del proyecto
Ampliar el uso del aprendizaje automático para lograr la eficiencia energética
En la batalla contra el cambio climático, resulta esencial que disminuyamos el consumo de recursos y energía a la vez que aumentamos la eficiencia general. El aprendizaje automático es una herramienta prometedora para alcanzar este objetivo y ya ha realizado contribuciones significativas en diversos sectores e industrias, lo cual ha generado avances vitales en la automatización y la eficiencia. El equipo del proyecto FUDIPO, financiado con fondos europeos, pretende extender la aplicación de prácticas de aprendizaje automático a todas las industrias europeas con el fin de mejorar drásticamente la eficiencia energética y de los recursos. Para lograrlo, en el proyecto se desarrollará una plataforma de optimización, tres demostradores de sistemas extensivos a todo el emplazamiento y dos demostradores tecnológicos a pequeña escala. Asimismo, gracias a la recopilación de datos y a las simulaciones se pueden conseguir mejoras sustanciales.
Objetivo
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.
Ámbito científico
- 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
- agricultural sciencesagriculture, forestry, and fisheriesforestry
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-SPIRE-2016
Régimen de financiación
RIA - Research and Innovation actionCoordinador
721 23 VASTERAAS
Suecia