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.
Endpoint Prediction Soft Sensor Model T-S Fuzzy System Structure Tuning and Uniform Design
Yuan Ping Feng Lin Mao Zhizhong
Northeasten University, Shenyang 110819, China
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
2174-2177
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）