Using BP Neural Network to Predict the Sinter Comprehensive Performance: FeO and Sinter Yield

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

doi:10.4028/www.scientific.net/AMR.771.209

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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: FeO and Sinter Yield

Abstract:

Sinter is the main raw material for ironmaking. It is very important to control sinter chemical composition and comprehensive performance. In this paper, a predictive system for sinter chemical composition FeO and the sinter yield 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.The application results show that the prediction system has high accuracy rate, stability and reliability, the sintering productivity was improved effectively.

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Periodical:

Advanced Materials Research (Volume 771)

Pages:

209-212

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.771.209

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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), FeO, Perdition, Sinter, Sinter Yield

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References

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