Endpoint Prediction of EAF Based on Multiple Support Vector Machines

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Abstract The endpoint parameters are very important to the process of EAF steel��making, but their on��line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines (MSVM). In this model, the input space was divided by subtractive clustering and a sub��model based on LS��SVM was built in each sub��space. To decrease the correlation among the sub��models and to improve the accuracy and robustness of the model, the sub��models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

Key words�� endpoint prediction EAF soft sensor model multiple support vector machine (MSVM) principal components regression (PCR)

Received: 01 January 1900

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	Articles by authors
	YUAN Ping
	MAO Zhi��zhong
	WANG Fu��li

Cite this article:

YUAN Ping,MAO Zhi��zhong,WANG Fu��li. Endpoint Prediction of EAF Based on Multiple Support Vector Machines[J]. Chinese Journal of Iron and Steel, 2007, 14(2): 20-0.

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http://gt.chinamet.cn/Jweb_gtyjxb_en/EN/ OR http://gt.chinamet.cn/Jweb_gtyjxb_en/EN/Y2007/V14/I2/20