Estimation of heat transfer coefficients in continuous casting under large disturbance by Gaussian kernel particle swarm optimization method

The work presented in this paper focuses on the estimation of the heat transfer coefficients by measured surface temperatures which contains large disturbances. In previous works on the calculation of heat transfer coefficients from the measured surface temperatures, the impact of large disturbance on the accuracy of the estimation of heat transfer coefficient was not considered. To solve this problem, we introduce an integrated approach which contains Gaussian Kernel (GK) function and the Particle Swarm Optimization (PSO) algorithm. Moreover, we use the real industrial data of the SAE 1800 slab from Baosteel Corporation to show the validity of this new approach. The simulation experiment results show that our GK-PSO method can reduce the influence of large disturbances effectively. Finally, we use the corrected heat transfer coefficients to improve the accuracy of the heat transfer model. The model can be used to predict the shell thickness of slabs, the predicted results are also validated by the actual measured data.