Environment 4.0 : How digitalization and machine learning can improve the environmental footprint of the steel production processes / Valentina Colla in MATERIAUX & TECHNIQUES, Vol. 108, N° 5-6 (2020)  

[article]  inMATERIAUX & TECHNIQUES > Vol. 108, N° 5-6 (2020) . - 11 p. Titre : Environment 4.0 : How digitalization and machine learning can improve the environmental footprint of the steel production processes Type de document : texte imprimé Auteurs : Valentina Colla, Auteur ; Costanzo Pietrosanti, Auteur ; Enrico Malfa, Auteur ; Klaus Peters, Auteur Année de publication : 2020 Article en page(s) : 11 p. Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Environnement -- Etudes d'impact Industrie 4.0 Le concept d’Industrie 4.0 correspond à une nouvelle façon d’organiser les moyens de production : l’objectif est la mise en place d’usines dites "intelligentes" ("smart factories") capables d’une plus grande adaptabilité dans la production et d’une allocation plus efficace des ressources, ouvrant ainsi la voie à une nouvelle révolution industrielle. Ses bases technologiques sont l'Internet des objets et les systèmes cyber-physiques. Industries sidérurgiques Intelligence artificielle Neutralité carbone Numérisation Index. décimale : 670.427 Mécanisation, automatisation, robotisation des opérations Résumé : The concepts of Circular Economy and Industrial Symbiosis are nowadays considered by policy makers a key for the sustainability of the whole European Industry. However, in the era of Industry4.0, this results into an extremely complex scenario requiring new business models and involve the whole value chain, and representing an opportunity as well. Moreover, in order to properly consider the environmental pillar of sustainability, the quality of available information represents a challenge in taking appropriate decisions, considering inhomogeneity of data sources, asynchronous nature of data sampling in terms of clock time and frequency, and different available volumes. In this sense, Big Data techniques and tools are fundamental in order to handle, analyze and process such heterogeneity, to provide a timely and meaningful data and information interpretation for making exploitation of Machine Learning and Artificial Intelligence possible. Handling and fully exploiting the complexity of the current monitoring and automation systems calls for deep exploitation of advanced modelling and simulation techniques to define and develop proper Environmental Decision Support Systems. Such systems are expected to extensively support plant managers and operators in taking better, faster and more focused decisions for improving the environmental footprint of production processes, while preserving optimal product quality and smooth process operation. The paper describes a vision from the steel industry on the way in which the above concepts can be implemented in the steel sector through some application examples aimed at improving socio-economic and environmental sustainability of production cycles. Note de contenu : - A view from the steel sector - Bridging the gap to the vision - Enabling circular economy and industrial symbiosis through digital transformation - Exemplar applications of advanced simulation and machine learning for monitoring and control of the environmental footpring - Digitalization enables CE and IS in steel business : the next steps Référence de l'article : 507 DOI : https://doi.org/10.1051/mattech/2021007 En ligne : https://www.mattech-journal.org/articles/mattech/pdf/2020/05/mt200062.pdf Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=35979 [article]

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