Endpoint prediction of electric arc furnace based on T-S fuzzy system

The endpoint parameters are very important to the process of electric arc furnace steel-making, but they are difficult to be measured on line. The soft sensor technology is widely used on the prediction of endpoint parameters. Based on the analysis of the smelting process and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters is established by T-S fuzzy system. A hybrid modeling method is proposed to construct the structure and to tune the parameters of T-S fuzzy model in this paper. Two steps were carried out: the establishment of an initial T-S fuzzy system by extracting rules in the total input space uniformly, and addition of new fuzzy rules to the system according to the Absolute Error index. Both the Levenberg-Marquardt method for nonlinear parameter optimization and the least squares method for linear parameter estimation were used to accelerate the computational convergence. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the T-S fuzzy system in the endpoint prediction.