AISTech 2022

Machine-Learning Model for Hydrogen Content Estimation During the Vacuum Degassing Process (Room 315)

17 May 22
2:30 PM - 3:00 PM

Tracks: Digitalization Applications

Speaker(s): Maria Argáez-Salcido, Data Scientist, ECON Tech; Nelson Sanchez, Smart Metrix Project Leader, ECON Tech

The vacuum degassing (VD) process decreases the hydrogen content in molten steel, improving the steel quality. The application of data analytics methods can help by predicting hydrogen content, achieving a reduction of time and energy consumption of the VD. For this reason, a machine-learning model was developed to estimate hydrogen content in molten steel, achieving a mean absolute error of 0.2400 and a mean square error of 0.0963 with the test set in the historical database. Statistics parameters display acceptable results, showing a robust model capable of generalizing the results for new data entry.