| Issue |
Matériaux & Techniques
Volume 108, Number 5-6, 2020
Materials and Society: transitions in society, materials and energy
|
|
|---|---|---|
| Article Number | 507 | |
| Number of page(s) | 11 | |
| Section | Environnement - recyclage / Environment - recycling | |
| DOI | https://doi.org/10.1051/mattech/2021007 | |
| Published online | 26 April 2021 | |
Regular Article
Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes☆
1
Scuola Superiore Sant’Anna, TeCIP Institute, ICT-COISP Center,
Pisa, Italy
2
Danieli Automation,
Buttrio (UD), Italy
3
Tenova S.p.A.,
Castellanza (VA), Italy
4
European Steel Technology Platform ASBL (ESTEP),
Brussels, Belgium
* e-mail: valentina.colla@santannapisa.it
Received:
4
October
2020
Accepted:
4
February
2021
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.
Key words: digitalization / industry 4.0 / Artificial Intelligence / machine learning / steel industry / environmental footprint / carbon neutrality
© SCF, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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