| Title |
A Machine Learning Framework for Improving Resources, Process, and Energy Efficiency Towards a Sustainable Steel Industry |
| ID_Doc |
63710 |
| Authors |
Martínez, AF; Muiños-Landín, S; Gordini, A; Ferrari, L; Chini, M; Bianco, L; Blaga, M |
| Title |
A Machine Learning Framework for Improving Resources, Process, and Energy Efficiency Towards a Sustainable Steel Industry |
| Year |
2024 |
| Published |
|
| Abstract |
In response to geopolitical instability, supply chain issues, and environmental concerns, initiatives like the European Green Deal highlight the need for a green transition in the EU industry. The steel sector, as an Energy-Intensive Industry, is crucial in this shift. This work introduces a Machine Learning framework for sustainability in the Steel Industry, addressing Resource, Process, and Energy efficiency with three ML algorithms. The framework, integrated into a Decision Support System, assists plant operators in the transition to a more sustainable process. |
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