Viscosity Prediction of CaO–SiO2–Al2O3–MgO System Slag Using MGGP
摘要
The viscosity of slag is an important factor for determining production and refining process of metals and the quality of products. The previously developed slag viscosity prediction models are mostly for solid-free slag. In this paper, we introduce a viscosity prediction model of CaO–SiO2–Al2O3–MgO system slag using a multi-gene genetic programming (MGGP). The obtained viscosity model is a simple algebraic equation with varying basicity, Al2O3 content, MgO content, and temperature of slag. Furthermore, the average relative error between experimental data and calculated values using the model is comparatively low with 25.10%. This model is applicable to slag within the range of CaO/SiO2 (0.2 ~ 6), Al2O3 (0 ~ 55%), and MgO (0 ~ 20%).Through the comparison with the experimental results and models of other researchers, it was confirmed that the accuracy of the new viscosity model is higher. The new model is also noteworthy for being able to predict the viscosity of solid-containing slag as well as solid-free slag.