The viscosity of slag is a very important factor in understanding the rate of metal-slag chemical reactions and the mass transfer taking place in the pyrometallurgy process. It is also vital for ensuring stable operation of metallurgical furnaces. Therefore, accurate prediction of the viscosity of slag is of great importance not only for the operation stability and productivity in the pyrometallurgy process but also for high yield.
Up to now, many researchers have carried out various viscometric experiments of slag, and many models have been developed to estimate its viscosity. However, the previously developed slag viscosity prediction models are mostly for solid-free slag.
Since most metallurgical slag necessarily contains MgO, it is very important to develop a model for predicting the viscosity of CaO–SiO2–Al2O3–MgO system slag.
Ro Tae Song, a researcher at the Faculty of Metallic Engineering, has proposed a viscosity prediction model of CaO–SiO2–Al2O3–MgO system slag using the multi-gene genetic programming (MGGP).
The proposed 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 the calculated values using the model is 25.10%, which is comparatively small.
For more information, please refer to his paper “Viscosity Prediction of CaO–SiO2–Al2O3–MgO System Slag Using MGGP” in “Transactions of the indian institute of metals” (SCI).