Application on one-key alloy feeding optimization system for steelmaking process of 90 t EAF
LI Bo1, YANG Ling-zhi1, SONG Jing-ling2, GUO Yu-feng1, LI Zhi-hui1
1. School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, Hunan, China; 2. Steelmaking Branch, Hengyang Valin Steel Tube Co., Ltd., Hengyang 421000, Hunan, China
摘要 电弧炉炼钢流程是钢铁行业转型的重要工艺路径。随着对特殊钢成分要求的不断提高,对钢铁企业电弧炉炼钢的成分控制提出了更高的要求。合金加料是电弧炉炼钢流程中调控成分的重要手段之一。由于冶炼不同钢种所需配入合金种类以及质量不同,各合金价价格不平均,合金成本一直是电弧炉炼钢冶炼生产成本的重要组成部分。当前,衡阳华菱钢管有限公司大管坯炼钢厂90 t电弧炉炼钢流程合金加料操作过程凭借现场技术人员经验,没有充分考虑不同钢种合金元素收得率差异、不同批次合金原料价格与成分的波动,造成窄成分控制差、合金加料次数多、合金成本高。针对这一问题,基于合金元素收得率动态库与2阶段单纯形算法对合金加料工艺的各影响因素进行分析,采用SQL数据库、Visual Studio 2013开发平台与OPC接口技术,研发了一键操作的合金加料优化系统,将系统计算结果传输至PLC,实现一键合金加料操作。系统运行结果表明,优化得出了不同钢种的合金元素收得率;电弧炉出钢后钢包合金配入比例与电弧炉炼钢流程合金配入比例相比,由73.88%左右提高到85.11%;电弧炉出钢后钢包内合金加料经系统计算成本降低了6.32%~8.07%,精炼炉合金加料经系统计算成本降低了1.04%~22.59%。电弧炉炼钢流程总成本降低了1.53%~10.02%。
Abstract:EAF steelmaking process is the important process on steel industry transformation path, as the standard of special steel components increasing, the higher requirement of composition control for EAF steelmaking in iron and steel enterprises has put forward, and alloy feeding EAF steelmaking process is one of the important means for regulating composition. Because of alloy type and quality needed in different steel grades during smelting are different and the price of alloy is not average, therefore, alloy cost is always been an important part of the production cost of EAF steelmaking. At present, the alloy feeding operation process of 90 t EAF steelmaking process in Hengyang Hualing Steel Tube Co., Ltd., relies on the experienced technical staff, and does not fully consider the difference of alloy element yield for different steels. The fluctuation of price and composition of alloy raw materials in different batches results in less control of narrow composition, more frequency of alloy feeding and high alloy cost. In order to solve this problem,based on the dynamic database of alloy element yield rate and the two-stage simplex algorithm, the influencing factors of alloy feeding process were analyzed in the article. Using SQL database, Visual Studio 2013 development platform and OPC interface technology, the alloy feeding optimization system with one-key operation was developed. The calculation results of the system were transmitted to PLC to realize the one-key alloy feeding operation. The system operation results show that the alloy element yield rates of different steels are optimized. The alloy allocation ratio in the ladle after EAF tapping increased from about 73.88% to 85.11%, compared with the whole steelmaking process. The system calculation cost of alloy feeding in ladle after EAF tapping reduced 6.32%-8.07% and the system calculation cost of alloy feeding in LF reduced 1.04%-22.59%. The total cost of EAF steelmaking process decreased by 1.53%-10.02%.
李勃, 杨凌志, 宋景凌, 郭宇峰, 李志慧. 90 t电弧炉炼钢流程一键合金加料优化系统应用[J]. 钢铁, 2022, 57(4): 58-67.
LI Bo, YANG Ling-zhi, SONG Jing-ling, GUO Yu-feng, LI Zhi-hui. Application on one-key alloy feeding optimization system for steelmaking process of 90 t EAF[J]. Iron and Steel, 2022, 57(4): 58-67.
[1] 李新创.新时代钢铁工业高质量发展之路[J].钢铁,2019,54(1):1.(LI Xin-chuang. Road map to high-quality development of iron and steel industry in new age[J]. Iron and Steel,2019,54(1):1.) [2] 姜周华,康从鹏,刘福斌,等.特种冶金生产流程的发展趋势[J].材料与冶金学报,2021,20(1):1.(JIANG Zhou-hua,KANG Cong-peng,LIU Fu-bing,et al. Development trend of special melting and remelting production process[J]. Journal of Materials and Metallurgy, 2021,20(1):1.) [3] 姜周华,姚聪林,朱红春,等.电弧炉炼钢技术的发展趋势[J].钢铁,2020,55(7):1.(JIANG Zhou-hua,YAO Cong-lin,ZHU Hong-chun,et al. Technology development trend in electric arc furnace steelmaking[J]. Iron and Steel, 2020,55(7):1.) [4] 王春梅,周东东,徐科,等.综述钢铁行业智能制造的相关技术[J].中国冶金,2018,28(7):1. (WANG Chun-Mei,ZHOU Dong-dong,XU Ke,et al. Review of intelligent manufacturing technology in steel industry[J]. China Metallurgy, 2018,28(7):1.) [5] 侯勇. LF炉合金加料系统优化模型研究及应用[D].西安:西安理工大学,2019.(HOU Yong. Research and Application of Optimization Model for Alloy Feeding System of LF Furnace[D].Xi'an: Xi'an University of Technology, 2019.) [6] 赵莹,李纯良,马义茹,等.基于多元非线性回归对合金收得率的预测[J].数学的实践与认识,2020,50(17):297.(ZHAO Ying,LI Chun-liang,MA Yi-ru,et al. Prediction of alloy yield based on multiple nonlinear regression[J]. Journal of Mathematics in Practice and Theory,2020,50(17):297.) [7] 倪洁.钢水配料方案优化的研究[J].中国金属通报,2019(6):116.(NI Jie. Research on optimization of molten steel batching scheme[J]. China Metal Bulletin,2019(6):116.) [8] 曹宇轩. LF炉精炼合金加料模型和温度预报模型开发与应用[D].武汉:武汉科技大学,2020.(CAO Yu-xuan. Development and Application of LF Furnace Refining Alloy Feeding Model and Temperature Forecast Model[D].Wuhan: Wuhan University of Science and Technology, 2020.) [9] 安剑奇,谢新鹏,雷琪,等. 精炼炉合金加料过程建模及自学习控制[C]//第26届中国过程控制会议(CPCC2015)论文集.北京:中国自动化学会过程控制专业委员会,2015:1.(AN Jian-qi,XIE Xin-peng,LEI Qi,et al. Modeling and self-learning control for alloy feeding process in refining furnace[C]//CPCC2015. Beijing: Process Control Committee of Chinese Association of Automation, 2015:1.) [10] 吴越怡,朱家明,刘辛邑,等.基于BP神经网络对合金收得率影响因素的研究[J].河南科技学院学报(自然科学版),2020,48(5):57.(WU Yue-yi,ZHU Jia-ming,LIU Xin-yi,et al. Research on influence factors of alloy yield based on BP neural network[J]. Journal of Henan Institute of Science and Technology(Natural Science), 2020,48(5):57.) [11] Anil Kumar Kothari,Rajeev Ranjan,Rama Shankar Singh, et al. A real-time ferroalloy model for the optimum ladle furnace treatment during the secondary steelmaking[J]. Ironmaking and Steelmaking,2019,46(3):211.) [12] 史云,贾荣,赵晓群.合金加料系统及智能控制[J].工业加热,2016,45(1):66.(SHI Yun,JIA Rong,ZHAO Xiao-qun.Alloy charging system and intelligent control[J]. Industrial Heating,2016,45(1):66.) [13] 刘强.分段配料法优化合金加料准确性的研究与应用[J].自动化与仪器仪表,2015(8):144.(LIU Qiang. Research and application of optimizing alloy feeding accuracy by segmented proportioning method[J]. Automation and Instrumentation, 2015(8):144.) [14] 肖爱达,李光强,谢世正,等.基于自适应模型的精炼炉合金加料智能控制系统[J].冶金自动化,2017,41(1):52.(XIAO Ai-da,LI Guang-qiang,XIE Shi-zheng,et al. Intelligent alloy charging control system for refining furnace based on adaptive model[J]. Metallurgical Industry Automation, 2017,41(1):52.) [15] 王龙,冀秀梅,刘玠.人工智能在钢铁工业智能制造中的应用[J].钢铁,2021,56(4):1.(WANG Long,JI Xiu-mei,LIU Jie.Application of artificial intelligence in intelligent manufacturing in steel industry[J]. Iron and Steel,2021,56(4):1.) [16] 吕明,李航,杨凌志,等.EBT区域底吹流量变化对电弧炉炼钢的影响[J].钢铁,2019,54(10):38.(LÜ Ming,LI Hang,YANG Ling-zhi,et al. Effect of bottom blowing flow rate near EBT area on EAF steelmaking[J].Iron and Steel,2019,54(10):38.) [17] 朱冉,任宣宇,姚琪,等.基于BP神经网络的合金收得率预测[J].电脑编程技巧与维护,2020(4):7.(ZHU Ran,REN Xuan-yu,YAO Qi,et al. Prediction of alloy yield based on BP neural network[J]. Software Development and Application, 2020(4):7.) [18] 李廷刚,陈勇,郑伟,等.基于BP神经网络的合金收得率预测模型[J].山西冶金,2019,42(3):15.(LI Ting-gang,CHEN Yong,ZHENG Wei,et al. Prediction model of alloy yield based on BP neural network[J]. Shanxi Metallugry, 2019,42(3):15.) [19] 杨凌志,王学义,王志东,等.基于收得率动态库的合金加料优化模型[J].北京科技大学学报,2014,36(增刊1):104.(YANG Ling-zhi,WANG Xue-yi,WANG Zhi-dong,et al. Alloy charging optimization model based on the yield dynamic libraries[J]. Journal of University of Science and Technology Beijing, 2014,36(s1):104.) [20] 王新江.中国电炉炼钢的技术进步[J].钢铁,2019,54(8):1.(WANG Xin-jiang. Technological progress of EAF steelmaking in China[J]. Iron and Steel, 2019,54(8):1.) [21] 吴耀光,肖步庆,朱立光,等.电炉炼钢钢铁原料的现状分析与展望[J].钢铁,2021,56(11):55.(WU Yao-guang, XIAO Bu-qing, ZHU Li-guang, et al. Current situation analysis and prospect of iron and steel raw material for electric arc furance steelmaking[J].Iron and Steel, 2021,56(11):55.) [22] 马国宏,宋景凌,杨凌志,等.基于单纯形法优化合金加料方案的研究[J].工业加热,2013,42(1):54.(MA Guo-hong,SONG Jing-ling,YANG Ling-zhi,et al. Research on alloy charging scheme optimization by using of the simplex method [J]. Industrial Heating, 2013,42(1):54.) [23] Peter Hedlund,Anders Gustavsson. Design and evaluation of an effective modified simplex method[J]. Analytica Chimica Acta,1999,391(3):257.) [24] 郭照庄,岳雅璠,孙月芳.单纯形法原理及其扩展[J].北华航天工业学院学报,2014,24(3):1.(GUO Zhao-zhuang,YUE Ya-fan,SUN Yue-fang. The principle of simplex method and its extension[J]. Journal of North China Institute of Aerospace Engineering, 2014,24(3):1.)