Prediction model of Molten Steel Temperature in LF
In the smelting process of Ladle Furnace, the steel temperature affects the LFs operation and rhythm of steel-making process. Based on the idea of increasing model, a case based reasoning (CBR) based temperature prediction model is proposed in this paper. In order to minimize the severe nonlinear correlation among the input parameters, to improve the accuracy and robustness of the model, the result of CBR is corrected by fuzzy least square support vector machines (FLS-SVM). The temperature prediction models accuracy is perfectly improved and the simulation results demonstrate the efficiency of the method. And the number of heats of with the predictive errors of end temperature of molten steel in LF are all not over 5 degrees centigrade is greater than 85％.
Ladle Furnace CBR support vector machine increasing model
YUAN Ping MAO Zhi-zhong WANG Fu-li
School of Information Science & Engineering, Northeast University Shenyang 110004, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3747-3751
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）