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VERSION:2.0
METHOD:PUBLISH
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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 15b
SUMMARY:Applying continuous hot metal temperature measurement on the data-driven model for blast furnace thermal state prediction 
DESCRIPTION:The ironmaking industry is nowadays facing the great challenge of process optimization and transformation regarding the current ecological and economic requirements. Since the blast furnace is still the main facility for metallic iron production, a stable blast furnace operation aiming at lowering reducing agent consumption remains the main target in the daily business. Thus, a well-controlled blast furnace thermal state should base on a reliable thermal state prediction. 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 data-driven models require not only high measurement accuracy of hot metal temperature measurement but also enough datasets. 
After the successful commissioning of the multi-wavelength pyrometer for continuous hot metal temperature measurement on ROGESA blast furnace NO. 5, a new machine learning model is developed and its performance is compared with the model that is based on traditional immersion lance measurement. This newly developed machine learning model will be integrated into the current running expert system (BFXpert) platform for developing a rule-based thermal control model

CLASS:PUBLIC
DTSTART:20230614T113000
DTEND:20230614T115000
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