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Aiming at the limitations of traditional thermal model and intelligent model,a new hybrid model is established for soft sensing of the molten steel temperature in LF.Firstly,a thermal model based on energy conservation is described;and then,an improved intelligent model based on process data is presented by ensemble ELM(extreme learning machine) for predicting the molten steel temperature in LF.Secondly,the self-adaptive data fusion is proposed as a hybrid modeling method to combine the thermal model with the intelligent model.The new hybrid model could complement mutual advantage of two models by combination.It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model.The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature.The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.
Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM (extreme learning machine) for predicting the molten steel temperature in LF. Secondarily, the self-adaptive data fusion is proposed as a hybrid modeling method to combine the thermal model with the intelligent model.The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. new new model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. These experiments demonstrate that the hybrid model has good gene ralization performance and high accuracy.