Computational modeling and prediction on viscosity of slags by big data mining

Abstract

The viscosity of slag is a key factor affecting metallurgical efficiency and recycling, such as metal-slag reaction and separation, as well as slag wool processing. In order to comprehensively clarify the variation of the slag viscosity, various data mining methods have been employed to predict the viscosity of the slag. In this study, a more advanced dual-stage predictive modeling approach is proposed in order to accurately analyze and predict the viscosity of slag. Compared with the traditional single data mining approach, the proposed method performs better with a higher recall rate and low misclassification rate. The simulation results show that temperature, SiO2, Al2O3, P2O5, and CaO have greater influences on the slag’s viscosity. The critical temperature for onset of the important influence of slag composition is 980 ℃. Furthermore, it is found that SiO2 and P2O5 have positive correlations with slag’s viscosity, while temperature, Al2O3, and CaO have negative correlations. A two-equation model of six-degree polynomial combined with Arrhenius formula is also established for the purpose of providing theoretical guidance for industrial application and reutilization of slag.