Methods and Tools of Improving Steel Manufacturing Processes: Current State and Future Methods

Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

Abstract

The steel industry is continuously looking for new ways to improve resource efficiency and sustainability due to high dependence on resources and increasing demand for more sustainable production. There are general tools and methodologies available and applicable for a wide range of production processes. The general tools and methodologies have similar characteristics as the domain specific tools needed or already utilized in the steel industry. However, there is still clear need for integration, further development, domain specific modelling, utilizing artificial intelligence and machine learning. This paper reviews the current state and recent developments of plant coordination & control, raw materials & energy optimization and quality management in steel industry and discusses the future methods and developments.
Original languageEnglish
Pages (from-to)1174-1179
Number of pages6
JournalIFAC-PapersOnLine
Volume52
Issue number13
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
Event9th IFAC Conference on Manufacturing Modelling, Management and Control - Berlin, Germany
Duration: 28 Aug 201930 Aug 2019

Fingerprint

Iron and steel industry
Steel
Quality management
Artificial intelligence
Learning systems
Sustainable development
Raw materials

Keywords

  • Steel industry
  • Plants
  • Process systems
  • Co-ordination
  • Control
  • Optimization
  • Quality

Cite this

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title = "Methods and Tools of Improving Steel Manufacturing Processes: Current State and Future Methods",
abstract = "The steel industry is continuously looking for new ways to improve resource efficiency and sustainability due to high dependence on resources and increasing demand for more sustainable production. There are general tools and methodologies available and applicable for a wide range of production processes. The general tools and methodologies have similar characteristics as the domain specific tools needed or already utilized in the steel industry. However, there is still clear need for integration, further development, domain specific modelling, utilizing artificial intelligence and machine learning. This paper reviews the current state and recent developments of plant coordination & control, raw materials & energy optimization and quality management in steel industry and discusses the future methods and developments.",
keywords = "Steel industry, Plants, Process systems, Co-ordination, Control, Optimization, Quality",
author = "Jere Backman and Vesa Kyll{\"o}nen and Heli Helaakoski",
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language = "English",
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issn = "2405-8971",
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Methods and Tools of Improving Steel Manufacturing Processes : Current State and Future Methods. / Backman, Jere (Corresponding Author); Kyllönen, Vesa; Helaakoski, Heli.

In: IFAC-PapersOnLine, Vol. 52, No. 13, 2019, p. 1174-1179.

Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

TY - JOUR

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AU - Kyllönen, Vesa

AU - Helaakoski, Heli

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Y1 - 2019

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AB - The steel industry is continuously looking for new ways to improve resource efficiency and sustainability due to high dependence on resources and increasing demand for more sustainable production. There are general tools and methodologies available and applicable for a wide range of production processes. The general tools and methodologies have similar characteristics as the domain specific tools needed or already utilized in the steel industry. However, there is still clear need for integration, further development, domain specific modelling, utilizing artificial intelligence and machine learning. This paper reviews the current state and recent developments of plant coordination & control, raw materials & energy optimization and quality management in steel industry and discusses the future methods and developments.

KW - Steel industry

KW - Plants

KW - Process systems

KW - Co-ordination

KW - Control

KW - Optimization

KW - Quality

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DO - 10.1016/j.ifacol.2019.11.355

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