基于BP神经网络的烧结矿性能预报模型

doi:10.13228/j.boyuan.issn0449-749x.20170031

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Abstract To solve the problem of neglevting the high temperature characteristics of iron ore powder in sinter forecast model， the assimilation reaction characteristic number and liquidity characteristic number which reflect the high temperature performance of iron power are added into the model. BP neural network is used to establish prediction model of sinter performance. The sinter indexes that affects the production of blast furnace were chose as the output. The high temperature and the physical and chemical properties of the iron ore powder were analyzed as input. Thus， the prediction model was established by BP neural network， and the algorithm of BP neural network was optimized. The structure of BP neural network for prediction model was 8-17-4. After training the neural network ， the prediction accuracy of the predicted characters was more than 85%， which meant that the neural network had good accuracy and adaptability.

Key words： iron ore fines Sinter properties high temperature performance characteristic number BP neural network predicton model

Received: 18 January 2017 Published: 07 September 2017

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	Articles by authors
	DIAO Lu-Peng
	WU Keng
	ZHU Li
	CHEN Xiao-Min
	QIN Xuan-Ke

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DIAO Lu-Peng,WU Keng,ZHU Li等. Prediction model of sinter properties based on BP neural network[J]. Iron and Steel, 2017, 52(9): 11-15.

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http://www.chinamet.cn/Jweb_gt/EN/10.13228/j.boyuan.issn0449-749x.20170031 OR http://www.chinamet.cn/Jweb_gt/EN/Y2017/V52/I9/11

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