Prediction model of molten steel temperature in LF

In the smelting process of Ladle Furnace, the steel temperature affects the LF´s 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 model´s 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%.