Digital optimization of refractory maintenance

Nikolaus Mutsam, Franz Pernkopf, Gregor Lammer

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Refractory condition monitoring and maintenance are key to extending the lifetime of a vessel lining and therefore increase steelmaking efficiency. We present an approach for optimizing refractory maintenance by employing statistics and machine learning methods. The scope of this work is to provide intelligent decision support for planning and performing maintenance on refractory material in order to improve its lifetime. We tackle this challenge by contriving a recommendation system for refractory maintenance, e.g. for proposing hot repair intervals, based on historic process data and lining measurements.

Original languageEnglish
Title of host publicationAISTech 2021 - Proceedings of the Iron and Steel Technology Conference
PublisherAssociation for Iron and Steel Technology
Pages1657-1666
Number of pages10
ISBN (Electronic)9781935117933
DOIs
Publication statusPublished - 2021
EventIron and Steel Technology Conference and Exposition: AISTech 2021 - Nashville, United States
Duration: 29 Jun 20211 Jul 2021

Publication series

NameAISTech - Iron and Steel Technology Conference Proceedings
Volume2021-June
ISSN (Print)1551-6997

Conference

ConferenceIron and Steel Technology Conference and Exposition
Country/TerritoryUnited States
CityNashville
Period29/06/211/07/21

Keywords

  • Automated maintenance
  • Industry 4.0
  • Refractory optimization

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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