Metall. Res. Technol.
Volume 118, Number 5, 2021
|Number of page(s)||8|
|Published online||21 September 2021|
BP neural network prediction for Si and S contents in hot metal of COREX process based on mathematical analysis and Deng’s correlation
State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, PR China
2 School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, PR China
3 Department of Iron and Steel Design and Research, MCC Huatian Engineering & Technology Corporation, Nanjing 210019, PR China
* e-mail: firstname.lastname@example.org
Accepted: 1 September 2021
In COREX operation, the Si and S contents in hot metal are relatively high and easy-fluctuating, which is one of the problems affecting the practical operation. Accurate predictions of Si and S contents can provide theoretical references for stabilizing the fluctuations and decreasing the contents of Si and S in hot metal. Therefore, the present work established the prediction model of Si and S contents in hot metal in COREX based on BP neural network. The results show that the root-mean-square errors between the predicted value and actual value for Si and S are 0.098 and 0.0037, respectively. They are 0.070 and 0.0040 when the time-sequence lapse method is adopted, which turns out to be better. Therefore, the model is accurate and reliable to predict the Si and S contents in hot metal in COREX.
Key words: COREX / BP neural network / hot metal / prediction
© EDP Sciences, 2021
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