Ladle Slide Gate Health Monitoring for Steel Industry: Overcoming the Industrialization Challenges of Data-Driven Diagnostics in Steel Plants

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dc.contributor.authorDisidoro, Fabio
dc.contributor.authorSchmidt, Verena
dc.contributor.authorMehmedovic, Adi
dc.contributor.authorSchöpe, Till
dc.contributor.authorNetsch, Christoph
dc.date.accessioned2024-09-02T13:58:13Z
dc.date.available2024-09-02T13:58:13Z
dc.date.issued2024
dc.description.abstractThe steel industry is undergoing a transformative phase with the advent of digitalization and automation technologies. A system was developed and implemented to track the condition of ladle slide gate refractories during operation. It aims at providing operators with timely insights on ladle slide gate conditions and guidance to support decision making at the ladle preparation area. Digital twins of refractory components consolidate data of different sources and process steps along their lifecycle. We will give insights on how data driven, classical as well as machine learning based models, support slide gate refractory plate condition assessments. Then, we will provide an outlook on how such models can be used to estimate the remaining lifetime. The framework and methodology presented in this paper aims to offer insights into overcoming the obstacles in the industrialization of data-driven solutions within the steel sector. The architecture selected streamlines the implementation of data-driven tools in the steel industry, considering the stringent data sharing requirements and the heterogenous infrastructure. Results will be presented demonstrating the application of the system to detect critical situations with the operation of ladle slide gate systems and support operators in decision making related to refractory components at the ladle preparation area.
dc.description.sponsorshipRHI Magnesita. Alpamayo (https://www.alpamayo-solutions.com/de)
dc.identifier.citation11th European Continuous Casting Conference. October 2024. Philharmonie Essen Conference Center.
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1485
dc.language.isoen
dc.titleLadle Slide Gate Health Monitoring for Steel Industry: Overcoming the Industrialization Challenges of Data-Driven Diagnostics in Steel Plants
dc.typeConference
dc.type.csemdivisionsBU-R
dc.type.csemresearchareasData & AI
dc.type.csemresearchareasIndustry 4.0
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