Metall. Res. Technol.
Volume 116, Number 2, 2019
|Number of page(s)||12|
|Published online||14 January 2019|
Modelling of viscosity of fluorine-free mold fluxes using neural network
School of Materials Science and Engineering, UNSW Australia,
2 School of Metallurgy and Material Engineering, Chongqing University of Science and Technology, 401331 Chongqing, PR China
3 College of Mechanics and Materials, Hohai University, 211100 Nanjing, PR China
4 Steelmaking Research Department, Baosteel Group Corporation Research Institute, 201900 Shanghai, PR China
* e-mail: email@example.com
Accepted: 31 May 2018
Viscosity is an important property of mold fluxes for steel continuous casting. However, direct measurement of viscosity of multi-component systems in a broad range of temperatures and compositions is an onerous work and has some limitations. This paper developed a model using the back propagation (BP) neural network to describe the viscosity of fluorine-free mold fluxes. The BP neural network model was developed and validated using 70 experimental values of viscosity of fluorine-free mold fluxes CaO-SiO2-Al2O3-B2O3-Na2O-TiO2-MgO-Li2O-MnO-ZrO2; 51 of them were used for developing the neural network model and the rest 19 viscosity data for the model validation. Calculated viscosities were in a good agreement with the experimental data. Based on the developed model, the effects of temperature and composition on the viscosity of fluorine-free fluxes were predicted and discussed.
Key words: mold flux / viscosity / neural network modelling
© EDP Sciences, 2019
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