A Machine Learning Framework for Improving Resources, Process, and Energy Efficiency Towards a Sustainable Steel Industry

被引：0

作者：

Fernandez Martinez, Andrea ^{[1
]}

Muinos-Landin, Santiago ^{[1
]}

Gordini, Angelo ^{[2
]}

Ferrari, Luca ^{[2
]}

Chini, Matteo ^{[3
]}

Bianco, Loris ^{[3
]}

Blaga, Mircea ^{[4
]}

机构：

[1] AIMEN Technol Ctr, Pontevedra, Spain

[2] OPTIT Srl, Bologna, Italy

[3] Ferriere Nord SpA, Osoppo, Italy

[4] Tenaris Silcotub SA, Calarasi, Romania

来源：

SMART TECHNOLOGIES FOR A SUSTAINABLE FUTURE, VOL 2, STE 2024 | 2024年 / 1028卷

基金：

欧盟地平线“2020”;

关键词：

Machine Learning; Sustainability; Steel Industry; Energy Efficiency; Process Optimization;

D O I：

10.1007/978-3-031-61905-2_1

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：3 / 10

页数：8

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