Title

ADVANCED DATA MINING AND MACHINE LEARNING FOR SMART REFRACTORY CONTROL

ADVANCED DATA MINING AND MACHINE LEARNING FOR SMART REFRACTORY CONTROL

Authorship

Lammer, Gregor; Forrer, Manuel; Feuerstein, Markus; Pernkopf, Franz

DOI

10.5151/2237-0234-31905

Downloads

heading.download_article_detail 973 Downloads

Abstract

"The full digitization of the industry promises significant efficiency gains. This development begins to have an impact on the operation in steel plants, when decisions are made based on traceable data. This paper presents an approach to discover patterns in big data sets and applying methods of artificial intelligence for interpretation. As example, the identification of the main refractory wear mechanism in the hot spots and improvements applying this approach will be presented. Further, we applied this intelligent system for process optimization to calculated to optimal campaign length considering production, maintenance and refractory parameters. The paper also examines and discusses the operational impact and future applications."

 

"The full digitization of the industry promises significant efficiency gains. This development begins to have an impact on the operation in steel plants, when decisions are made based on traceable data. This paper presents an approach to discover patterns in big data sets and applying methods of artificial intelligence for interpretation. As example, the identification of the main refractory wear mechanism in the hot spots and improvements applying this approach will be presented. Further, we applied this intelligent system for process optimization to calculated to optimal campaign length considering production, maintenance and refractory parameters. The paper also examines and discusses the operational impact and future applications."

Keywords

Machine learning, Big data, Refractory systems, AI, Smart scheduling, Condition monitoring

Machine learning, Big data, Refractory systems, AI, Smart scheduling, Condition monitoring