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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Acknowledgments
This work was performed in the framework of the RETROFEED Project granted by the European Union’s Horizon 2020 research and innovation programme under grant agreement N.869939.
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Fernández Martínez, A. et al. (2024). A Machine Learning Framework for Improving Resources, Process, and Energy Efficiency Towards a Sustainable Steel Industry. In: Auer, M.E., Langmann, R., May, D., Roos, K. (eds) Smart Technologies for a Sustainable Future. STE 2024. Lecture Notes in Networks and Systems, vol 1028. Springer, Cham. https://doi.org/10.1007/978-3-031-61905-2_1
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DOI: https://doi.org/10.1007/978-3-031-61905-2_1
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