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| Hybrid Modeling for Soft Sensing of Molten Steel Temperature in LF |
| TIAN Hui��xin1,MAO Zhi��zhong2,3,WANG An��na2 |
| 1. School of Electrical and Automation Engineering, Tianjin Polytechnic University, Tianjin 300160, China; 2. School of Information Science and Engineering, Northeastern University, Shenyang 110004, Liaoning, China; 3. Key Laboratory of Integrated Automation of Process Industry of Ministry of Education, Northeastern University, Shenyang 110004, Liaoning, China |
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Abstract 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.
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Received: 01 January 1900
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2009, Vol. 16 
