Prediction Model of Micum Strength for Iron Ore Sinter

Abstract:

Micum strength is an important indicator of quality of sinter; BP artificial neural network model is built to predict the strength of sinter drum. The neural network use the main factors that influence the sinter drum as input data, and output is Micum strength. Experiment results shows that the maximum absolute error between the Micum strength predicted by neural network and real value from the sinter plant is 0.3346, and the average absolute error is 0.1154. These prove that the prediction is accuracy. In addition, because of the "black box" characteristic of the neural network model, the neural network model can not give the law of how the various factors affect the micum strength of sinter ore, this paper also uses the model to analysis the law of how TFe, SiO₂ content affect the micum strength. The results not only consist with the sintering theory, but also verify the validity of the model.

• Periodical: Advanced Materials Research (Volume 402)
• Pages: 476-479
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.402.476
• Online since: November 2011
• Authors: Wei Wang, Zhi Hui Xu, Long Long Yang, Zheng Liang Xue, Dong Nan Zhao, Sheng Qiang Song
• Keywords: Drum Strength, Sinter, Sinter Drum

References

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