包钢高炉炼铁优化配矿平台的开发与应用

doi:10.13228/j.boyuan.issn0449-749x.20210406

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Abstract In order to realize the integration and collaborative analysis of blast furnace ironmaking system from iron ore procurement and blast furnace ore blending optimization, this paper develops a blast furnace ironmaking whole system and whole process optimization ore blending platform for the front ironmaking system. The whole process refers to the whole process from iron ore procurement to blast furnace molten iron output. The whole system refers to the overall system of all blast furnace ironmaking in iron and steel enterprises. The optimized ore blending platform includes four parts, which are database system, single blast furnace optimized ore blending platform, whole system blast furnace optimized ore blending platform and production data acquisition and analysis platform. The platform takes iron ore as the starting point, based on the calculation of blast furnace ironmaking process and material balance, uses planning solution, linear regression, multivariate nonlinear regression, neural network and other algorithms to establish data analysis and calculation model, and forms optimal ore blending decision through statistical analysis of data. The system can realize the integrated optimization of iron ore procurement, raw ore processing, mineral processing, sintering, pellet and blast furnace process, and provides the iron ore procurement and configuration scheme that meets the requirements of blast furnace production and has the lowest cost under specific time and specific conditions. At the same time, the cost of intermediate products and molten iron raw materials in the blast furnace system can be calculated by optimizing ore distribution platform, and the deviation between production cost and planned cost also can be calculated. Managers can adjust and optimize the ore blending scheme according to the deviation of comparative data and the change of market conditions. The application of the platform in Baotou Steel realizes the intelligent, digital and accurate management of iron ore procurement and configuration. Under the premise of meeting the requirements of blast furnace production, the raw material cost of molten iron and the procurement cost of iron ore in Baotou Steel are reduced by optimizing the distribution of platform, which brings considerable economic and social benefits to the enterprise.

Key words： blast furnace ore blending optimization ironmaking cost system

Received: 16 July 2021

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	Articles by authors
	HE Xiao-yi
	LIU Zhou-li
	WU Sheng-li
	ZHAO Bin
	YANG Fan

Cite this article:

HE Xiao-yi,LIU Zhou-li,WU Sheng-li等. Development and application of optimized ore blending platform for blast furnace ironmaking system in Baotou Steel[J]. Iron and Steel, 2022, 57(2): 28-35.

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http://www.chinamet.cn/Jweb_gt/EN/10.13228/j.boyuan.issn0449-749x.20210406 OR http://www.chinamet.cn/Jweb_gt/EN/Y2022/V57/I2/28

[1] 许满兴.优化高炉炼铁原料采购与烧结高炉配矿一体化的思路和方法[J].烧结球团,2016,41(5):1.(XU Man-xing. Ideas and methods for optimizing the integration of raw material procurement for blast furnace ironmaking and ore blending for sintering blast furnace[J].Sintered Pellet,2016,41(5):1.)
[2] 储满生,王宏涛,柳正根.高炉炼铁过程数学模拟的研究进展[J].钢铁,2014,49(11):1.(CHU Man-sheng, WANG Hong-tao, LIU Zheng-gen. Research progress on mathematical simulation of blast furnace ironmaking process[J]. Iron and Steel, 2014, 49 (11): 1.)
[3] 陈伟,武鹏飞,王宝祥,等.烧结-高炉全过程炉料结构智能优化系统[J].烧结球团,2020,45(5):8.(CHEN Wei, WU Peng-fei, WANG Bao-xiang, et al. Intelligent optimization system of burden structure in the whole process of sintering and blast furnace[J]. Sintering Pellet, 2020, 45(5): 8.)
[4] 谢运强,张中中,王子宏,等. 某钢铁企业常用铁矿粉烧结基础性能及优化配矿[J]. 中国冶金, 2018, 28(3): 9.(XIE Yun-qiang,ZHANG Zhong-zhong,WANG Zi-hong,et al. Sintering basic characteristics of iron ores used in an iron and steel plant and optimum proportioning ores[J]. China Metallurgy, 2018, 28(3): 9.)
[5] 杨世峰, 裴未迟, 刘小杰, 等. 承钢高炉炼铁原燃料数据库的设计与构建[J]. 中国冶金, 2021, 31(5): 86. (YANG Shi-feng, PEI Wei-chi, LIU Xiao-jie, et al. Design and construction of ironmaking raw fuel database of blast furnace for Chengde Steel Mill[J]. China Metallurgy, 2021, 31(5):86.)
[6] 范晓慧, 曾垂喜, 姜涛, 等. 铁矿石烧结性能预报模型[J].中南大学学报(自然科学版), 2005, 36(6): 949.(FAN Xiao-hui, ZENG Chui-xi, JIANG Tao, et al. Predictivemodel of iron ore sintering capabilities[J]. Journal of Central South University(Science and Technology), 2005, 36(6):949.)
[7] 范晓慧.铁矿粉造块数学模型与专家系统[M]. 北京:科学出版社,2013.(FAN Xiao-hui. The Mathermatical Model and Expert System of Iron Ore Building Block[M]. Beijing:Science Press,2013.)
[8] CHEN Xiao-xia,SHE Jin-hua,CHEN Xin,et al. Modeling method of carbon efficiency in iron ore sintering process[C]//2016 IEEE Internationnal Conferenrence on Industrial Technology (ICIT).Taibei:IEEE,2016:103.
[9] 傅菊英,姜涛,朱德庆. 烧结球团学[M]. 长沙:中南工业大学,1996.(FU Jü-ying,JIANG Tao,ZHU De-qing.Principle of Sintering and Pelletizing[M]. Changsha: Centeral South University of Technology Press,1996.)
[10] 李云涛. 烧结优化配矿模型的研究[D]. 长沙: 中南大学, 2004.(LI Yun-tao. The Research of Sintering Optimal Matchingsystem[D]. Changsha: Central South University,2004.)
[11] 刘振林, 蔡漳平. 高铁低硅烧结矿配矿技术的试验研究[J].钢铁, 2001, 36(12): 1.(LIU Zhen-lin, CAI Zhang-ping. Experimental investigation ofproportioning ore technique for high iron and low siliconsinter[J]. Iron and Steel, 2001, 36(12): 1.)
[12] 吴胜利,刘宇,杜建新,等.铁矿石的烧结基础特性之新概念[J].北京科技大学学报,2002,28(3):254.(WU Sheng-li, LIU Yu, DU Jian-xin, et al. New concept of basic sintering properties of iron ore[J]. Journal of Beijing University of Science and Technology, 2002,28(3): 254.)
[13] 吴胜利,王军,杜建新,等. 冶金研究[M].北京:冶金工业出版社,2003.(WU Sheng-li, WANG Jun, DU Jian-xin, et al. Metallurgical Research[M].Beijing: Metallurgical Industry Press, 2003.)
[14] 罗国萍,孙国龙,赵艳霞,等.包钢常用铁矿粉烧结基础特性[J]. 包钢科技,2008,8(1):198.(LUO Guo-ping,SUN Guo-long,ZHAO Yan-xia,et al.Research on basic characteristics of iron charge used for sintering in Baotou steel[J].Science and Techology of Baotou Steel,2008,8(1):198.)
[15] 苏布新,张建良,常健,等.铁矿粉的烧结特性及优化配矿试验研究[J].钢铁,2010,46(9):22.(SU Bu-xin, ZHANG Jian-liang, CHANG Jian,et al. Sintering characteristics of iron ores and experiment study on optimizing ores and experiment study on optimizing ore-blending[J].Iron and Steel,2010,46(9):22.)
[16] 邵慧君.基于EIman神经网络的烧结矿质量在线预测研究[J].仪表技术与传感器,2017(10):98.(SHAO Hui-jun. Online prediction of sinter quality based on EIman neural network[J]. Instrument Technology and Sensor, 2017(10):98.)
[17] 韩庆红,金永龙,张军红.BP网络在烧结矿质量预测种的初步应用[J].烧结球团,2005,30(1):20.(HAN Qing-hong, JIN Yong-long, ZHANG Jun-hong. Preliminary application of BP network in sinter quality prediction[J]. Sinter Pellet, 2005, 30 (1): 20.)
[18] 吴胜利,韩宏亮,徐少兵. 高炉优化配料数学模型的研究[J].钢铁,2007,42(9):19.(WU Sheng-li, HAN Hong-liang, XU Shao-bing. Study on mathematical model of blast furnace burden optimization[J]. Iron and Steel, 2007, 42 (9): 19.)
[19] 刘代飞. 烧结过程工艺参数优化模型的研究[D]. 长沙: 中南大学, 2004.(LIU Dai-fei. The Research of Optimal Model for Sinteringprocessing Parameters[D]. Changsha: Central South University, 2004.)
[20] Pinheiro P, 杨天勇. CVRD 的烧结工艺铁矿石评估方法[J]. 现代冶金, 2002, 30(2): 4.(Pinheiro P, YANG Tian-yong. Evaluating ways of sintering process iron ore of CVRD[J]. Modern Metallurgy, 2002, 30(2);4.)
[21] 肖鹏.高炉炼铁技术创新实践及未来展望[J].钢铁,2021,56(6):10.(XIAO Peng.Innovation practice and future prospects of blast furnace ironmaking technology[J].Iron and Steel,2021,56(6):10.)
[22] 刘玠.人工智能推动冶金工业变革[J].钢铁,2020,55(6):1.(LIU Jie. Artificial intelligence drives changes in metallurgical industry[J].Iron and Steel,2020,55(6):1.)