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Reliable BOF endpoint prediction by novel data-driven modeling

In a cooperative effort between SMS Siemag AG (SMS), AG der Dillinger Hüttenwerke (DH), and the Chair for Artificial Intelligence of the TU Dortmund University (TUD), modern data mining and machine learning algorithms have been closely integrated with steel processing by a newly developed modular, distributed and scalable data-stream processing system. It can be easily extended to read and analyze data from all kinds of sensors and data sources. Its analysis can readily be applied to metallurgical and manufacturing processes. The new approach makes use of a variety of static and dynamic process and measurement variables and uses the precision of data mining and its adaptability to the 190 t converters of DH in order to predict target variables, such as the temperature or the phosphorus content of the melt at the end of blowing. Rough physical conditions can lead to sensor failures and deterioration. Use is made of statistical methods and multiple models based on different process in order to cope with these technical issues.