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.