Regular Article

Modelling of viscosity of fluorine-free mold fluxes using neural network

¹ School of Materials Science and Engineering, UNSW Australia, 2052 Sydney, 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: j.q.zhang@unsw.edu.au

Received: 3 April 2018
Accepted: 31 May 2018

Abstract

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.