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ORGANIZER;CN='8th ECIC & 9th ICSTI 2022':MAILTO:info@ecic-icsti.com
LOCATION:Room „Focke Wulf“
SUMMARY:Using AI and digital twins to improve blast furnace operations
DESCRIPTION:Using AI and Digital Twins to Improve Blast Furnace Operations
  
Julie Kim, Nicholas Aubry, Mitren Sukhram and Yale Zhang

Abstract:
Successfully managing an ironmaking plant is a complex task. The need to satisfy the competing interests of efficiency, quality, and cost, while adhering to ever-higher standards of safety and sustainability puts immense pressure on people and systems. To meet the stringent expectations for today’s blast furnace operation, operational intelligence is a must for plant managers, process engineers, and shop-floor operators. Hatch’s Digital Twin Platform fulfills this need; it delivers operational intelligence from three strongly linked aspects: integration, intelligence and interaction. In this paper, a use case of a blast furnace digital twin is presented. The case focuses on predicting the thermal state of blast furnace using artificial intelligence. Advanced outlier filtering and stacked machine learning models, are used together with fundamental blast furnace mass and energy calculations. The model provides operators with a consistent understanding of the furnace thermal state, which results in a better hot metal temperature and silicon control strategy.  The paper concludes by summarizing best practices learned from blast furnace digital twin development and deployment.  

Key words: Blast furnace, Ironmaking, Digital Twin, Artificial Intelligence

CLASS:PUBLIC
DTSTART:20220901T103000
DTEND:20220901T105500
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