Prediction model of C and Mn alloy yield in LF based on AO-ENN
YI Zhen1,2, CHAI Lin1,2, LIU Hui-kang1,2, YANG Lei1,2
1. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; 2. Engineering Research Center of Metallurgical Automation and Measurement Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China
Abstract：LF is an important equipment in iron and steel smelting. Its main purpose is to adjust the alloy composition in steel. However, at present, most of actual production still use manual experience to adjust the alloy composition, and the effect of existing alloy charging model is not satisfactory. In order to make the added alloy more accurate and further reduce the cost, an alloy yield prediction model based on Aquila Optimizer(AO) to optimize Elman neural network(ENN) was designed in this paper. Firstly, the factors that have a great impact on the alloy yield were analyzed according to the correlation. Then, the Elman neural network optimized by Aquila Optimizer was used to establish the alloy yield prediction model. Finally, the amount of alloy to be added was calculated through the predicted alloy yield. The simulation experiment was carried out using the real data in the actual production. The simulation results show that the AO-ENN model established in this paper has smaller error and higher precision than BP model and Elman model, which has certain guiding significance for the alloy addition in the actual production.
易振, 柴琳, 刘惠康, 杨磊. 基于AO-ENN的LF炉C、Mn合金收得率预报模型[J]. 中国冶金, 2022, 32(5): 40-48.
YI Zhen, CHAI Lin, LIU Hui-kang, YANG Lei. Prediction model of C and Mn alloy yield in LF based on AO-ENN[J]. China Metallurgy, 2022, 32(5): 40-48.
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2022, Vol. 32 
