Demonstration of a Novel AI-Based Approach for Digitalization Ladle Metallurgy Facility

In 2023, a major steel manufacturer initiated a pilot project aimed at the digital transformation of a Ladle Metallurgy Facility (LMF). The project, supported by governmental and technological partnerships, focused on addressing the digitalization challenges within heavy industries, which often lag behind other manufacturing sectors. This study explores the implementation of advanced digital technologies, including machine learning-based video analytics, novel sensing technologies, Industrial Internet of Things (IIoT) integration, real-time cognitive computing, big data analytics, and digital twins. The primary objective of this initiative was to enhance process efficiency by reducing manual interventions, minimizing process variability, and improving the final metallurgical properties of the steel. This work provides valuable insights into the digitalization process, technical execution, data standardization requirements, and workforce impact in a heavy manufacturing environment. The results demonstrate the potential of these technologies to advance operational efficiency in the steelmaking industry, laying the groundwork for future technology trials.

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Publication Details

Published
2024-12-05
DOI
https://doi.org/10.1109/icir64558.2024.10976823
Primary Topic
Metallurgical Processes and Thermodynamics
Type
article
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article

Demonstration of a Novel AI-Based Approach for Digitalization Ladle Metallurgy Facility

Esraa Hassan, Amir Eaman
Metallurgical Processes and Thermodynamics
article

Demonstration of a Novel AI-Based Approach for Digitalization Ladle Metallurgy Facility

Esraa Hassan, Amir Eaman
article en

Abstract

In 2023, a major steel manufacturer initiated a pilot project aimed at the digital transformation of a Ladle Metallurgy Facility (LMF). The project, supported by governmental and technological partnerships, focused on addressing the digitalization challenges within heavy industries, which often lag behind other manufacturing sectors. This study explores the implementation of advanced digital technologies, including machine learning-based video analytics, novel sensing technologies, Industrial Internet of Things (IIoT) integration, real-time cognitive computing, big data analytics, and digital twins. The primary objective of this initiative was to enhance process efficiency by reducing manual interventions, minimizing process variability, and improving the final metallurgical properties of the steel. This work provides valuable insights into the digitalization process, technical execution, data standardization requirements, and workforce impact in a heavy manufacturing environment. The results demonstrate the potential of these technologies to advance operational efficiency in the steelmaking industry, laying the groundwork for future technology trials.

Acadia University (CA)
Openalex Percentile: Top 71%
Metallurgical Processes and Thermodynamics
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