1 School of Metallurgy, Northeastern University, Shenyang 110819, Liaoning, China 2 Jianlong Acheng Iron & Steel Co., Ltd., Harbin 150000, Heilongjiang, China
An online BOF terminal temperature control model based on big data learning
1 School of Metallurgy, Northeastern University, Shenyang 110819, Liaoning, China 2 Jianlong Acheng Iron & Steel Co., Ltd., Harbin 150000, Heilongjiang, China
摘要 The development of basic oxygen furnace (BOF) intelligent steelmaking model based on artificial intelligence and big data is the focus of international research and development. In the view of the current situation that the BOF cannot continuously detect the composition and molten steel temperature, combined with the monitoring results of the high-definition and high-brightness camera at the converter mouth, an online BOF terminal temperature control model is established based on big data learning case-based reasoning model and expert system model. The on-site online operation shows that the model can effectively improve the ‘‘flying lance’’ phenomenon and the splashing condition, the stability and safety of smelting process are better than that of artificial smelting, the ‘‘flying lance’’ rate decreases from 39.2% to 0, the early splashing rate decreases from 21.4% to 13.3% and the late splashing rate decreases from 81.25% to 56.7%. When the temperature fluctuation is controlled at ± 15 ºC, the hit rate of the terminal temperature under the automatic control of the model is 90.91%.
Abstract：The development of basic oxygen furnace (BOF) intelligent steelmaking model based on artificial intelligence and big data is the focus of international research and development. In the view of the current situation that the BOF cannot continuously detect the composition and molten steel temperature, combined with the monitoring results of the high-definition and high-brightness camera at the converter mouth, an online BOF terminal temperature control model is established based on big data learning case-based reasoning model and expert system model. The on-site online operation shows that the model can effectively improve the ‘‘flying lance’’ phenomenon and the splashing condition, the stability and safety of smelting process are better than that of artificial smelting, the ‘‘flying lance’’ rate decreases from 39.2% to 0, the early splashing rate decreases from 21.4% to 13.3% and the late splashing rate decreases from 81.25% to 56.7%. When the temperature fluctuation is controlled at ± 15 oC, the hit rate of the terminal temperature under the automatic control of the model is 90.91%.
Jia-wei Guo,Dong-ping Zhan,Guo-cai Xu, et al. An online BOF terminal temperature control model based on big data learning[J]. Journal of Iron and Steel Research International, 2023, 30(05): 875-886.
2023, Vol. 30 
