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Anais do Seminário de Automação & TI

ISSN 2594-5335

22° Seminário de Automação e TI — vol. 22, num.22 (2018)

Título

ADVANCED DATA MINING AND MACHINE LEARNING FOR SMART REFRACTORY CONTROL

ADVANCED DATA MINING AND MACHINE LEARNING FOR SMART REFRACTORY CONTROL

Autoria

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

Lammer, Gregor
Lammer, Gregor

Eu sou esse autor

Forrer, Manuel
Forrer, Manuel

Eu sou esse autor

Feuerstein, Markus
Feuerstein, Markus

Eu sou esse autor

Pernkopf, Franz
Pernkopf, Franz

Eu sou esse autor

DOI

10.5151/2237-0234-31905

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Resumo

"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."

Palavras-chave

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

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

Como citar

Lammer, Gregor; Forrer, Manuel; Feuerstein, Markus; Pernkopf, Franz. ADVANCED DATA MINING AND MACHINE LEARNING FOR SMART REFRACTORY CONTROL , p. 219-230. In: 22° Seminário de Automação e TI, São Paulo, 2018.
ISSN: 2594-5335 , DOI 10.5151/2237-0234-31905