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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 language | English |
|---|---|
| Title of host publication | AISTech 2021 - Proceedings of the Iron and Steel Technology Conference |
| Publisher | Association for Iron and Steel Technology |
| Pages | 1657-1666 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781935117933 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | Iron and Steel Technology Conference and Exposition: AISTech 2021 - Nashville, United States Duration: 29 Jun 2021 → 1 Jul 2021 |
Publication series
| Name | AISTech - Iron and Steel Technology Conference Proceedings |
|---|---|
| Volume | 2021-June |
| ISSN (Print) | 1551-6997 |
Conference
| Conference | Iron and Steel Technology Conference and Exposition |
|---|---|
| Country/Territory | United States |
| City | Nashville |
| Period | 29/06/21 → 1/07/21 |
Keywords
- Automated maintenance
- Industry 4.0
- Refractory optimization
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
Fields of Expertise
- Information, Communication & Computing
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