A machine learning model to predict non-metallic inclusion dissolution in the metallurgical slag

Abstract

Dissolution of non-metallic inclusions in the metallurgical slag is of vital importance for cleanliness control of the steel manufacturing. With the development of high temperature confocal laser scanning microscope (HT-CLSM), new insights have been obtained due to its in situ observation characteristics, higher resolution and precise control. However, HT-CLSM measurement has the limitation, eg the slag composition cannot include high amount of transition metal oxides. In addition, it is time consuming for the experimental procedure and not so simple to succeed for every measurement. It is known that digitalisation has made a significant progress in recent years. Machine learning (ML), a sub-domain of artificial intelligence (AI), is the key enabling technology for the digitalisation of the material science and industry. The database for ML model is collected using almost all available HT-CLSM experimental data and subsequently the established database is trained by different ML methods. Unseen data is used as the benchmark of the ML model. Al2O3 dissolution is the main process to be predicted in the current study. A good agreement between the HT-CLSM data and the ML model prediction results show the possibility to apply ML in process metallurgy.