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VERSION:2.0
METHOD:PUBLISH
BEGIN:VEVENT
ORGANIZER;CN='8th ECIC & 9th ICSTI 2022':MAILTO:info@ecic-icsti.com
LOCATION:Room „Danzig“
SUMMARY:Improving blast furnace thermal control by integrating continuous hot metal temperature measurement
DESCRIPTION:The blast furnace is nowadays still the main facility for metallic iron production in steel making. Because of the current economic and ecological situation, a stable operation aiming at lowing fuel consumption is strongly required. Thus, reliable thermal state prediction plays an important role in thermal control. Compared to the silicon content in hot metal, the hot metal temperature is considered to be more representative of the current thermal state. Hence, for model fine-tuning, many thermal state prediction models especially machine learning models require not only high measurement accuracy of hot metal temperature measurement but also enough datasets.
 
This paper introduces first a short overview of methods of hot metal temperature measurement and presents secondly the model-based thermal control system applied at ROGESA. To further improve this system, continuous hot metal temperature measurements are installed for each of the two tap holes on blast furnace No.5. The results are compared with the conventional hot metal temperature measurements that are taken by immersion thermocouple probes and integrated into the current thermal control system for further improvement of thermal state prediction supported by machine learning. 

Key words: thermal control; hot metal temperature prediction; continuous hot metal temperature measurement; machine learning models 

CLASS:PUBLIC
DTSTART:20220831T134500
DTEND:20220831T141000
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END:VCALENDAR