An online BOF terminal temperature control model based on big data learning
作者机构：School of MetallurgyNortheastern UniversityShenyang 110819LiaoningChina Jianlong Acheng Iron&Steel Co.Ltd.Harbin 150000HeilongjiangChina
出 版 物：《Journal of Iron and Steel Research International》 (国际钢铁研究杂志)
年 卷 期：2023年第30卷第5期
页 面：875-886页
核心收录：
学科分类：080602[工学-钢铁冶金] 08[工学] 0806[工学-冶金工程]
基 金：the support from the Open Competition Scientific and Technological Research Projects of Heilongjiang Province(2022ZXJ03A02) Jiangxi Provincial Technical Innovation Guidance Program(20202BDH80002)
主 题：Basic oxygen furnace Case-based reasoning Expert system Steelmaking Temperature
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 *** 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 *** on-site online operation shows that the model can effectively improve theflying lancephenomenon and the splashing condition,the stability and safety of smelting process are better than that of artificial smelting,theflying lancerate 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%.
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