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

doi:10.33313/377/111

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

Institute of Signal Processing and Speech Communication (4420)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-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 language	English
Title of host publication	2019 AISTech Conference Proceedings
Pages	1081-1089
Number of pages	9
DOIs	https://doi.org/10.33313/377/111
Publication status	Published - 1 Jan 2019
Event	Iron and Steel Technology Conference and Exposition: AISTech 2019 - Pittsburgh, United States Duration: 6 May 2019 → 9 May 2019

Conference

Conference	Iron and Steel Technology Conference and Exposition
Country/Territory	United States
City	Pittsburgh
Period	6/05/19 → 9/05/19

Keywords

AI
Condition monitoring
Life time prediction
Process data
Refractory
RH degasser

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.33313/377/111

Cite this

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title = "Refractory condition monitoring and lifetime prognosis for Rh degasser",

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.",

keywords = "AI, Condition monitoring, Life time prediction, Process data, Refractory, RH degasser",

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

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T1 - Refractory condition monitoring and lifetime prognosis for Rh degasser

AU - Viertauer, Andreas

AU - Mutsam, Nikolaus

AU - Pernkopf, Franz

AU - Gantner, Andreas

AU - Grimm, Georg

AU - Winkler, Waltraud

AU - Lammer, Gregor

AU - Ratz, Alexander

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - AI

KW - Condition monitoring

KW - Life time prediction

KW - Process data

KW - Refractory

KW - RH degasser

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DO - 10.33313/377/111

M3 - Conference paper

AN - SCOPUS:85071315686

SP - 1081

EP - 1089

BT - 2019 AISTech Conference Proceedings

T2 - Iron and Steel Technology Conference and Exposition

Y2 - 6 May 2019 through 9 May 2019

ER -

Refractory condition monitoring and lifetime prognosis for Rh degasser

Abstract

Conference

Keywords

ASJC Scopus subject areas

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