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Machine-Learning Model for Hydrogen Content Estimation During the Vacuum Degassing Process

Abstract:

The vacuum degassing (VD) process decreases the hydrogen content in molten steel, improving steel quality. One problem is that no tool allows real-time monitoring of hydrogen content in molten steel. The application of data analytics methods can help pbkp_redict hydrogen content, reducing the 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 0.1298, 0.2400, and 0.2179 ppm for training, test, and production data, respectively. Furthermore, statistics parameters display acceptable results, showing a robust model capable of generalizing the results for new data entry. This real-time pbkp_rediction allowed the decrease of hydrogen content in the final product by helping the operator to make better decisions through the visualization of live operational variables. Thus, generating recommendations of process variables, increasing the quality of the manufactured steel, increasing the productivity of the vacuum degassing process, and reducing energy consumption in the steel-making process.