Using BP Neural Network to Predict the Sinter Comprehensive Performance: TFe and Fuel Consumption

Wei Chen; Bao Xiang Wang; Ying Chen; Hui Juan Zhang; Xing Li

doi:10.4028/www.scientific.net/AMR.771.213

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Using BP Neural Network to Predict the Sinter Comprehensive Performance: TFe and Fuel Consumption
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 771Using BP Neural Network to Predict the Sinter...

Using BP Neural Network to Predict the Sinter Comprehensive Performance: TFe and Fuel Consumption

Abstract:

The principal objective of blast furnace is to produce high quality molten iron at a high rate with a low consumption. It is very important to control sinter chemical composition and comprehensive performance. This is because the sinter is the main raw material for ironmaking. In this paper, a predictive system for sinter chemical composition TFe and the solid fuel consumption was established based on BP neural network, which was trained by actual production data. The MATLAB m file editor was used to write code directly in this paper. Practical application shows the applications of the system not only can reduce the work difficulty of technical personnel, but also can improve the hit ratio of production index and the productivity.

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Periodical:

Advanced Materials Research (Volume 771)

Pages:

213-216

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.771.213

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Online since:

September 2013

Authors:

Wei Chen, Bao Xiang Wang, Ying Chen, Hui Juan Zhang, Xing Li

Keywords:

BP Neural Network (BPNN), Fuel Consumption, Prediction, Sinter, Tfe

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References

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