Refractory condition monitoring and lifetime prognosis for Rh degasser

Andreas Viertauer, Nikolaus Mutsam, Franz Pernkopf, Andreas Gantner, Georg Grimm, Waltraud Winkler, Gregor Lammer, Alexander Ratz

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


In the steelmaking industry, there is a demand for process optimization and predictability of the refractory based on information recorded during the production process [1–3]. The amount of recorded data has recently increased dramatically and machine learning and artificial intelligence (AI) techniques are exploited to filter out useful information for modeling the production process and the most influential parameters [4–6]. In this paper, the aim is to predict the refractory life-time based on data acquired during production. In total, 110 process parameters were preselected to build a statistical model to determine the influence on refractory wear by using machine learning techniques.
Original languageEnglish
Title of host publication2019 AISTech Conference Proceedings
Number of pages9
Publication statusPublished - 1 Jan 2019
EventIron and Steel Technology Conference and Exposition: AISTech 2019 - Pittsburgh, United States
Duration: 6 May 20199 May 2019


ConferenceIron and Steel Technology Conference and Exposition
Country/TerritoryUnited States


  • AI
  • Condition monitoring
  • Life time prediction
  • Process data
  • Refractory
  • RH degasser

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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