Regular Article

BP neural network prediction for Si and S contents in hot metal of COREX process based on mathematical analysis and Deng’s correlation

¹ 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: mingyinkou@gmail.com

Received: 26 January 2021
Accepted: 1 September 2021

Abstract

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.