2019 Pittsburgh AISTech

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Refractory Condition Monitoring and Lifetime Prognosis for RH Degassers (Room 410)

This paper will describe an approach for AI in secondary steelmaking for an RH degasser. The main goal is to get a refractory lifetime prognosis by understanding the correlation between refractory wear and defined process parameters using machine learning techniques. The first step is to correlate the process data with lining data to check whether any correlation can be found. The second step is to link the on-line monitored process data with the vessel shell temperature measured by an infrared-based system. The mathematical modeling, data processing and lifetime prognosis in real time will be briefly described.