Prediction model of end-point phosphorus content of converter based on FA-ELM
GAO Fang1, BAO Yan-ping1, WANG Min1, LIU Yu2, HUANG Yong-sheng2, SUN Guang-tao2
1. State Key Lab of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China; 2. The Third Steelmaking Plant, Zenith Steel Group Co., Ltd., Changzhou 213011, Jiangsu, China
Abstract:Accurate prediction of end-point phosphorus content in converter is of great significance to realize automatic tapping and improve converter smelting efficiency. In order to realize the narrow window control of end-point phosphorus content in converter and provide guidance for operation process, it is necessary to establish a more accurate prediction model of end-point phosphorus content. Based on the analysis of the smelting process and dephosphorization thermodynamics, 12 observable indexes affecting the dephosphorization process are selected to build the end-point phosphorus content prediction index system. Then, by using factor analysis (FA) to reduce the dimension of the data, six derived variables are obtained, which are used as the model input and the end-point phosphorus content is used as the model output, and the end-point phosphorus content prediction model based on the overrun learning machine (ELM) is established. By comparing the prediction results of the ELM model with the BP neural network, it is found that the ELM model has a higher fitting degree, R2 = 0.778 7, MAPE=0.106 0, and the furnace number with prediction error within ±0.003 0% accounts for 86.67%. Therefore, compared with the BP neural network model, the ELM model has higher accuracy and better generalization ability.
高放, 包燕平, 王敏, 刘宇, 黄永生, 孙光涛. 基于FA-ELM的转炉终点磷含量预测模型[J]. 钢铁, 2020, 55(12): 24-30.
GAO Fang, BAO Yan-ping, WANG Min, LIU Yu, HUANG Yong-sheng, SUN Guang-tao. Prediction model of end-point phosphorus content of converter based on FA-ELM[J]. Iron and Steel, 2020, 55(12): 24-30.
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