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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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.
Originalspracheenglisch
Titel2019 AISTech Conference Proceedings
Seiten1081-1089
Seitenumfang9
DOIs
PublikationsstatusVeröffentlicht - 1 Jan 2019
VeranstaltungAISTech 2019 Iron and Steel Technology Conference and Exposition - Pittsburgh, USA / Vereinigte Staaten
Dauer: 6 Mai 20199 Mai 2019

Konferenz

KonferenzAISTech 2019 Iron and Steel Technology Conference and Exposition
LandUSA / Vereinigte Staaten
OrtPittsburgh
Zeitraum6/05/199/05/19

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    ASJC Scopus subject areas

    • !!Industrial and Manufacturing Engineering

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    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 (S. 1081-1089). [PR-377-111] https://doi.org/10.33313/377/111