Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining

Minerals

Juan Yang¹  Ao Huang²  Huazhi Gu²  Yanzhu Huo²  Guangqiang Li² 

[1] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China;The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, China;

关键词: slag;    data mining;    viscosity;    big data;    predictive modeling;   

DOI  :  10.3390/min10030257

来源: DOAJ

【 摘 要 】

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 °C. 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.

【 授权许可】

Unknown