基于大数据技术的炉缸状态可视化

doi:10.13228/j.boyuan.issn0449-749x.20200536

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摘要为了更好地监测炉缸工作状态的变化,建立了高炉炉缸状态可视化系统,以展示炉缸热电偶温度、热流强度和炉缸活性的历史趋势和实时监控。收集了某高炉相关参数的数据,使用拉依达准则进行粗大异常值的处理并采用线性插值法进行了空缺值的填补,对热电偶温度和热流强度进行了分区域监测和计算并据此分析了炉缸炉底的侵蚀情况,最后将焦炭质量、渣铁成分和操作参数作为输入变量,提出了融合大数据技术的炉缸活性定量模型。大数据技术为钢铁行业的发展提供了新思路,进一步推动了高炉智能化炼铁。

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关键词 ：炉缸, 可视化系统, 热电偶温度, 热流强度, 炉缸活性

Abstract：In order to better monitor the change of the working state of the hearth, a visualization system of the hearth state of the blast furnace was established to simulate the historical trend and real-time monitoring of the hearth thermocouple temperature, heat flux intensity, and hearth activity. The data of the related parameters of a blast furnace were collected, and the rough outliers were treated by the Pauta criterion and the blank values were filled by the linear interpolation method. The thermocouple temperature and heat flux intensity were monitored and calculated in different regions, and the erosion of the hearth bottom was analyzed. The coke quality, slag iron composition, and operating parameters were taken as input variables, and a quantitative model of hearth activity incorporating big data technology was proposed. Big data technology provides new ideas for the development of the iron and steel industry and further promotes the intelligent ironmaking of blast furnaces.

Key words： hearth visualization system thermocouple temperature heat flow strength activity of hearth

收稿日期: 2020-10-20

引用本文:

张伟阳, 刘小杰, 李宏扬, 卜象平, 程相文, 吕庆. 基于大数据技术的炉缸状态可视化[J]. 钢铁, 2021, 56(7): 38-46. ZHANG Wei-yang, LIU Xiao-jie, LI Hong-yang, BU Xiang-ping, CHENG Xiang-wen, LÜ Qing. Visualization of hearth status based on big data technology[J]. Iron and Steel, 2021, 56(7): 38-46.

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[1] 于勇. 环境监管趋严促进钢铁行业绿色发展[N]. 中国环境报, 2018-08-31(003).(YU Yong. Stricter environmental supervision will promote green development of the steel industry[N]. China Environment News, 2018-08-31(003).)
[2] 殷瑞钰. 改革开放40年与中国钢铁工业技术进步[J]. 中国钢铁业, 2019, 191(1):6.(YIN Rui-yu. 40 years of reform and opening up and technological progress in China′s steel industry[J]. Chinese steel industry,2019,191(1):6.)
[3] GAO Zheng-kai, ZHAO Cheng-ping, WU Yun-ying, et al. Plug-In Kiln Video Camera and Image Pro-cessing System:USA, 7719564B2[P].2003-12-31.
[4] Akihiko Shinotake, Hitoshi Nakamura,Nariyuki Yadoumaru, et al. Investigation of blast-furnace hearth sidewall erosion by core sample analysis and consideration of campaign operation[J]. ISIJ International, 2003, 43(3): 321.
[5] JIAO Ke-Xin, ZHANG Jian-Liang, LIU Zheng-Jian.Analysis of blast furnace hearth sidewall erosion and protective layer formation[J].ISIJ International,2016,56(11): 1956.
[6] GAO Zheng-kai, LIU Jian, GAO Tai. Advanced inside-furnace monitoring techniques implemented on the new large blast furnace of Shagang[J]. AISTech Iron Steel Technol, 2010, 1: 565
[7] ZHAO Yong-fu, Jerry C Capo, Steven J McKnight, et al. Development of burden distribution technology at U S Steel Canada′s Hamilton Works′E′blast furnace[J].Iron and Steel Technology, 2011, 8 (1):52.
[8] 高征铠, 高泰. 高炉可视化与仿真技术的创新和实践[J]. 中国冶金,2013,23(2):8.(GAO Zheng-kai, GAO Tai. Innovation and practice of visualization and simulation technology of blast furnace[J].China Metallurgy, 2013,23(2):8.)
[9] 石琳, 程素森, 孙亮. 高炉炉缸三维侵蚀形状的可视化技术[J]. 冶金自动化, 2005,25(1): 5.(SHI Lin, CHENG Su-sen, SUN Liang. 3D visualization technology of erosional shape for blast furnace hearth[J]. Metallurgical Industry Automation, 2005,25(1): 5.)
[10] 赵东明, 李建军, 曾宇, 等. 鞍钢4号高炉炉墙结厚原因及解决措施[J]. 鞍钢技术, 2019(1):48.(ZHAO Dong-ming, LI Jian-jun, ZENG Yu, et al. Causes and solutions of wall thickening of No. 4 BF at Anshan Iron and Steel Co[J]. Angang Technology, 2019(1):48.)
[11] 曹英杰, 张建良,国宏伟, 等. 基于TwoStep算法的国丰1号高炉操作炉型聚类分析与应用[J]. 钢铁,2013,48(10):17.(CAO Ying-jie, ZHANG Jian-liang, GUO Hong-wei, et al.Cluster analysis and application of operating furnace type of Guofeng No.1 BF based on TwoStep algorithm[J]. Iron and Steel, 2013,48(10):17.)
[12] 顾东洋. 基于数据驱动的高炉冶炼操作参数优化[D]. 内蒙古:内蒙古科技大学,2014.(GU Dong-yang. Operation Parameter Optimization of Blast Furnace Smelting Based on Data Drive[D].Nei Mongol: Inner Mongolia University of Science and Technology, 2014.)
[13] 李洋龙. 京唐5 500 m³高炉炉缸工作状态研究[D]. 北京: 北京科技大学,2016.(LI Yang-long. Working State of Jingtang 5 500 m³ Blast Furnace Hearth[D]. Beijing: University of Science and Technology Beijing, 2016.)
[14] 马富涛,张建良,张磊, 等.铁前数模技术进展与大数据应用探讨[J].钢铁,2018,53(12):1.(MA Fu-tao, ZHANG Jian-liang, ZHANG Lei, et al. Introduction of development and progress of mathematical modeling technology in iron-making area and discussion on application prospects of big data technology[J]. Iron and Steel, 2018, 53(12):1.)
[15] 张龙来, 陈永明, 徐万仁, 等. 大型高炉的炉缸活性与炉缸侧壁侵蚀[C]// 第十四届全国大高炉炼铁学术年会论文集.嘉峪关: 第十四届全国大高炉炼铁学术年会, 2013:152.(ZHANG Long-lai, CHEN Yong-ming, XU Wan-ren, et al. Hearth activity and hearth sidewall erosion of large blast furnace[C]// Proceedings of the 14th National Large Blast Furnace Ironmaking Academic Annual Meeting. Jiayuguan: The 14th National Large Blast Furnace Ironmaking Academic Annual Meeting, 2013:152.)
[16] Brian E Dixon, Jon Duke, Shaun Grannis. Measuring and improving the quality of data used for syndromic surveillance[J]. Online Journal of Public Health Informatics, 2017, 9(1):182.
[17] 刘栋梁, 宋春晖, 高艳伟, 等. 高炉热负荷区域冷却壁热流强度及渣皮厚度的确定方法[P]. 中国:CN107622154A,2018-01-23.(LIU Dong-liang, SONG Chun-hui, GAO Yan-wei, et al. A Method for Determining the Heat Flux Strength and Slag Thickness of Blast Furnace Cooling Wall in Hot Load Area[P]. China:CN107622154A,2018-01-23.)
[18] 邓勇,刘然,刘小杰,等.炉缸炭砖侵蚀过程及基于AHP的界面反应调控[J].钢铁,2020,55(8):175.(DENG Yong, LIU Ran, LIU Xiao-jie, et al. Erosion process of carbon brick in hearth and control of interface reaction based on AHP[J]. Iron and Steel. 2020,55(8):175.)
[19] Girish Chandrashekar, Ferat Sahin. A survey on feature selection methods [J]. Computers and Electrical Engineering, 2014, 40 (1):16.
[20] 王涓, 吴旭鸣, 王爱凤. 应用皮尔逊相关系数算法查找异常电能表用户[J]. 电力需求侧管理, 2014,16(2):52.(WANG Juan, WU Xu-ming, WANG Ai-feng. Pearson correlation coefficient algorithm was used to find abnormal electricity meter users[J].Power Demand Side Management,2014,16(2):52.)
[21] SONG Ren-jie, CHEN Yu-ming. Fuzzy synthetic evaluation of relay protection based on variable weight value [J]. International Journal of Multimedia and Ubiquitous Engineering, 2015, 10(11): 143.