Composition Engineering on the Local Structure and Viscosity of the CaO-SiO₂-Al₂O₃-P₂O₅-FeO Slag by Machine Learning Methods

Abstract

Due to the high cost and low accuracy of high-temperature tests, the viscosity data for multicomponent slag systems is difficult to be obtained precisely. Therefore, it is important to fulfill the viscosity database of the multicomponent slag systems via reasonable methods with lower costs. In this study, a viscosity prediction method based on the machine learning method was proposed for the CaO-SiO2-FeO-Al2O3-P2O5 quinary slag system. To provide valid data for the machine learning model, the viscosity predicted by the molecular dynamic method and multiple semi-empirical models were compared to verify the applicability of these methods to the slag system. Different machine learning models were also developed. The results showed that the prediction results from the gradient boosting decision tree method were the most accurate for the CaO-SiO2-FeO-Al2O3-P2O5 quinary slag system. Based on this method, a color-map concerning the numerical effect of Al2O3 and P2O5 contents and slag viscosity is provided, which also provides assistance for the composition engineering to fulfill a certain demand on the viscosity design.

Keywords

• metallurgical slag
• machine learning
• viscosity
• molecular dynamic
• Pearson correlation coefficient