Forecast for Low Temperature Reduction Disintegration Properties of Vanadium-titanium Sinter Based on BP Neural Network

Xin Zicheng; Li Jie; Liu Weixing; Yang Aimin; Zhang Yuzhu; Wang Lili

doi:10.7513/j.issn.1004-7638.2017.03.017

Volume 38 Issue 3

Jun. 2017

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Xin Zicheng, Li Jie, Liu Weixing, Yang Aimin, Zhang Yuzhu, Wang Lili. Forecast for Low Temperature Reduction Disintegration Properties of Vanadium-titanium Sinter Based on BP Neural Network[J]. IRON STEEL VANADIUM TITANIUM, 2017, 38(3): 94-99. doi: 10.7513/j.issn.1004-7638.2017.03.017

Citation:

Xin Zicheng, Li Jie, Liu Weixing, Yang Aimin, Zhang Yuzhu, Wang Lili. Forecast for Low Temperature Reduction Disintegration Properties of Vanadium-titanium Sinter Based on BP Neural Network[J]. IRON STEEL VANADIUM TITANIUM, 2017, 38(3): 94-99. doi: 10.7513/j.issn.1004-7638.2017.03.017

Citation:

Xin Zicheng, Li Jie, Liu Weixing, Yang Aimin, Zhang Yuzhu, Wang Lili. Forecast for Low Temperature Reduction Disintegration Properties of Vanadium-titanium Sinter Based on BP Neural Network[J]. IRON STEEL VANADIUM TITANIUM, 2017, 38(3): 94-99. doi: 10.7513/j.issn.1004-7638.2017.03.017

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Forecast for Low Temperature Reduction Disintegration Properties of Vanadium-titanium Sinter Based on BP Neural Network

doi: 10.7513/j.issn.1004-7638.2017.03.017

1. College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063009, Hebei, China;
2. College of Continuing Education, North China University of Science and Technology, Tangshan 063009, Hebei, China;
3. College of Science, North China University of Science and Technology, Tangshan 063009, Hebei, China

Received Date: 2017-02-10

Abstract

Abstract

In order to improve the RDI_+3.15 of vanadium-titanium sinter,BP neural network algorithm was applied to the prediction of low temperature reduction degradation of vanadium-titanium sinter. The samples of the indicator data were divided into input samples and output samples,the input samples includes carbon,basicity,w （Mg O） and FMG ore,and the output sample was low temperature reduction degradation of vanadium-titanium sinter. The relationship between input samples and output samples was explored by using BP neural network algorithm. The results show that BP neural network model is suitable for studying on the low temperature reduction disintegration properties of sinter. It can predict the output samples effectively according to the input samples,and the average relative error is 5.7%,meeting the requirement of prediction precision in engineering practice,which provides guidance for the production of vanadium-titanium sinter.
- sinter,
- low temperature reduction disintegration properties,
- neural network model,
- carbon content,
- basicity,
- w(MgO)