80 t转炉终点预报TSVR模型精度

doi:10.13228/j.boyuan.issn0449-749x.20190499

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Abstract Basic oxygen furnace (BOF) steelmaking is a complex process with high-temperature physicochemical reactions. The composition of steel cannot be detected continuously during smelting, accurately predicting the carbon mass percent and temperature of the end-point is very meaningful to improve the end-point hit rate. The actual production samples of 80 t BOF were collected from one steel plant in Guangxi province. A twin support vector regression (TSVR) prediction model was established to realize the prediction of end-point temperature and carbon mass percent. The training process was carried out by using actual production samples of 100 heats, and the other 30 heats were adopted to verify the accuracy of the prediction model. The results show that the hit rate of the model with Δw([C])≤0.01% is 93.3％ and the hit rate of the model with Δt≤15 ℃ is 96.7％. In addition, the double hit rate of the model is 90%. By comparing with the BP neural network model, the end-point carbon content and the end-point temperature hit rate of this model are higher than those of the BP neural network model.

Key words： BOF steelmaking prediction model twin support vector regression accuracy

Received: 13 November 2019

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	Articles by authors
	WANG Miao
	LI Sheng-li
	GAO Chuang
	FAN Yue

Cite this article:

WANG Miao,LI Sheng-li,GAO Chuang等. End-point prediction TSVR model accuracy of 80 t BOF steelmaking[J]. Iron and Steel, 2020, 55(7): 53-57.

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http://www.chinamet.cn/Jweb_gt/EN/10.13228/j.boyuan.issn0449-749x.20190499 OR http://www.chinamet.cn/Jweb_gt/EN/Y2020/V55/I7/53

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