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 contributionResearchpeer-review

Abstract

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
Pages1081-1089
Number of pages9
DOIs
Publication statusPublished - 1 Jan 2019
EventAISTech 2019 Iron and Steel Technology Conference and Exposition - Pittsburgh, United States
Duration: 6 May 20199 May 2019

Conference

ConferenceAISTech 2019 Iron and Steel Technology Conference and Exposition
CountryUnited States
CityPittsburgh
Period6/05/199/05/19

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Keywords

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

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Viertauer, A., Mutsam, N., Pernkopf, F., Gantner, A., Grimm, G., Winkler, W., ... Ratz, A. (2019). Refractory condition monitoring and lifetime prognosis for Rh degasser. In 2019 AISTech Conference Proceedings (pp. 1081-1089). [PR-377-111] https://doi.org/10.33313/377/111