Discussion on intelligent manufacturing of sintering system and application of big data technology
LIU Song1, ZHAO Ya-di1, GAN Li1, FENG Wei1, LI Fu-min2, LÜ Qing2
1. Department of Computer Science and Technology, Tangshan College, Tangshan 063000, Hebei, China; 2. College of Metallurgy and Energy, North China University of Science and Technlogy, Tangshan 063210, Hebei, China
Abstract：In order to improve the intelligent manufacturing level of the sintering process, the research progress about the sintering system model in recent decades was systematically summarized. For the current problems in the models of sintering endpoint prediction, composition and quality prediction of sinter ore, and batching optimization, the prediction and optimization of the parameter in the sintering system were investigated by using big data, integrated learning and deep learning. Accordingly, the remarkable results in terms of the improvement in the prediction accuracy and the generalization ability were also emphatically introduced. Moreover, based on the parameter prediction model mentioned above, the hardware and software structure design methods of the parameter prediction and optimization system for on-site application were put forward. Finally, starting from the needs of the iron and steel industry, the point of view, the further integration of advanced information technology and industrial automation equipment was an important way to improve the level of intelligent manufacturing of sintering systems was analyzed, and the research direction and application prospects of big data and artificial intelligence technology in the sintering were also discussed.
刘颂, 赵亚迪, 甘丽, 冯伟, 李福民, 吕庆. 烧结系统智能制造与大数据技术应用探讨[J]. 钢铁, 2021, 56(10): 54-64.
LIU Song, ZHAO Ya-di, GAN Li, FENG Wei, LI Fu-min, LÜ Qing. Discussion on intelligent manufacturing of sintering system and application of big data technology[J]. Iron and Steel, 2021, 56(10): 54-64.
[1] 黄阳华. 德国“工业4.0”计划及其对我国产业创新的启示[J]. 经济社会体制比较, 2015(2): 1. (HUANG Yang-hua. German "Industry 4.0" plan and its enlightenment to China's industrial innovation[J]. Economic and Social System Comparison, 2015(2): 1.) [2] 李金华. 德国“工业4.0”与“中国制造2025”的比较及启示[J]. 中国地质大学学报(社会科学版), 2015,15(5):71.(LI Jin-hua. Comparison and Enlightenment between German "Industry 4.0" and "Made in China 2025"[J]. Journal of China University of Geosciences (Social Sciences), 2015, 15(5): 71.) [3] Wouter L, John W. Dealing with data: Upgrading infrastructure[J]. Science, 2011, 331(6024): 1515. [4] 张福明. 智能化钢铁制造流程信息物理系统的设计研究[J]. 钢铁, 2021, 56(6): 1.(ZHANG Fu-ming. Research and design on cyber physics system of intelligent iron and steel manufacturing process[J]. Iron and Steel, 2021, 56(6): 1.) [5] 姚林, 王军生. 钢铁流程工业智能制造的目标与实现[J]. 中国冶金, 2020, 30(7): 1. (YAO Lin, WANG Jun-sheng. Goal and realization of smart manufacturing in steel industry[J]. China Metallurgy, 2020, 30(7): 1.) [6] Schlechtingen M, Santos I F. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection[J]. Mechanical Systems and Signal Processing, 2011, 25(5): 1849. [7] LEI Y G, ZUO M J. Gear crack level identification based on weighted K nearest neighbor classification algorithm[J]. Mechanical Systems and Signal Processing, 2009, 23(5): 1535. [8] 刘文仲. 中国钢铁工业智能制造现状及思考[J]. 中国冶金, 2020, 30(6): 1. (LIU Wen-zhong. Current situation and thinking of intelligent manufacturing in China's iron and steel industry[J]. China Metallurgy, 2020, 30(6): 1.) [9] 王新东. 以“绿色化、智能化、品牌化”为目标规划设计河钢唐钢新区[J]. 钢铁, 2021, 56(2): 12. (WANG Xin-dong. Planning and design of HBIS Group Tangsteel new district with goal of "Green, Intelligent and Brand"[J]. Iron and Steel, 2021, 56(2): 12). [10] 张洁, 高亮, 秦威, 等. 大数据驱动的智能车间运行分析与决策方法体系[J]. 计算机集成制造系统, 2016, 22(5): 1220. (ZHANG Jie, GAO Liang, QIN Wei, et al. Analysis and decision-making method system driven by big data[J]. Computer Integrated Manufacturing Systems, 2016, 22(5): 1220.) [11] Moslehi K, Kumar R. A reliability perspective of the smart grid[J]. IEEE Transactions on Smart Grid, 2010, 1(1): 57. [12] 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.) [13] 张东霞, 苗新, 刘丽平, 等. 智能电网大数据技术发展研究[J]. 中国电机工程学报, 2015, 35(1): 2. (ZHANG Dong-xia, MIAO Xin, LIU Li-ping, et al. Research on big data technology development of smart grid[J]. Chinese Journal of Electrical Engineering, 2015, 35(1): 2.) [14] 颉建新,张福明. 钢铁制造流程智能制造与智能设计[J]. 中国冶金, 2019, 29(2): 1. (XIE Jian-xin,ZHANG Fu-ming. Intelligent manufacturing and intelligent design of iron and steel manufacturing process[J]. China Metallurgy, 2019, 29(2): 1.) [15] 邱卫云. 智能交通大数据分析云平台技术[J]. 中国交通信息化, 2013(10): 106. (QIU Wei-yun. Intelligent transportation big data analysis cloud platform[J]. China Traffic Information, 2013 (10): 106.) [16] 张红, 王晓明, 曹洁, 等. 基于大数据的智能交通体系架构[J]. 兰州理工大学学报, 2015, 41(2): 112. (ZHANG Hong, WANG Xiao-ming, CAO Jie, et al. Intelligent transportation architecture based on big data[J]. Journal of Lanzhou University of Technology, 2015, 41(2): 112.) [17] Usamentiaga R, Molleda J, Garcia D F, et al. Monitoring sintering burn-through point using infrared thermography[J]. Sensors, 2013, 13(8): 10287. [18] HU J Q, Rose E. Predictive fuzzy control applied to the sinter strand process[J]. Control Engineering Practice, 1997, 5(2): 247. [19] FENG Q M, LI T, FAN X H, et al. Adaptive prediction system of sintering through point based on self-organize artificial neural network[J].Transactions of Nonferrous Metals Society of China, 2000, 10(6): 804. [20] Nath N K, Mitra K. Optimization pressure for iron ore sintering by genetic algorithm[J]. Ironmaking and Steelmaking, 2004, 31(3): 199. [21] 李明河, 孙雁飞. 烧结终点模糊控制应用探讨[J].安徽工业大学学报(自然科学版),2004,21(3): 193. (LI Ming-he, SUN Yan-fei. Study of the fuzzy control system for burning through point of sintering[J]. Journal of Anhui University of Technology(Natural Science), 2004, 21(3): 193.) [22] 寇传乾. 基于废气温度上升点的烧结终点预报系统[J]. 烧结球团, 2007, 32(5): 27. (KOU Chuan-qian. Sintering end point prediction system based on the rising point of exhaust gas temperature[J]. Sintering Pellets, 2007, 32(5): 27.) [23] Vescovi M R, Lamega M M, Farquhar A. Modeling and simulation of a complex industrial process[J]. IEEE Expert, 1997, 12(3): 42. [24] Cortes C, Vapnik V. Support-vector networks[J]. Machine learning, 1995, 20(3), 273. [25] HUANG X X, FAN X H, CHEN X L, et al. Bed permeability state prediction model of sintering process based on data mining technology[J]. ISIJ International, 2016, 56(12):2113. [26] WU M, DUAN P, CAO W H, et al. An intelligent control system based on prediction of the burn-through point for the sintering process of an iron and steel plant[J]. Expert Systems Application, 2012, 39(5): 5971. [27] 范晓慧, 冯婧, 陈许玲, 等. 烧结矿化学成分预报模型与控制指导专家系统[J]. 矿冶工程, 2011, 31(4): 77. (FAN Xiao-hui, FENG Jing, CHEN Xu-ling, et al. Expert system for forecasting and controlling the chemical composition of sinter[J]. Mining and Metallurgical Engineering, 2011, 31(4): 77.) [28] Cumming M J, Thurlby J A. Developments in modelling and simulation of iron ore sintering[J]. Ironmak Steelmak, 1990, 17(4): 245. [29] 龙红明, 范晓慧, 陈许玲, 等. 基于神经网络预报的烧结矿化学成分控制专家系统[J]. 北京科技大学学报, 2006, 28(9): 867. (LONG Hong-ming, FAN Xiao-hui, CHEN Xu-ling, et al. Expert system for chemical composition control of sinter based on neural network prediction[J]. Journal of Beijing University of Science and Technology, 2006, 28(9): 867.) [30] 申炳昕, 范晓慧, 孙文东. 基于神经网络的烧结矿化学成分自适应预报系统的开发[J]. 烧结球团, 2002, 27(5): 1. (SHEN Bing-xin, FAN Xiao-hui, SUN Wen-dong. Development of an adaptive prediction system for sinter chemical composition based on neural network[J]. Sintering Pellets, 2002, 27(5): 1.) [31] 宋强, 王爱民. 基于 LS-SVM 的烧结矿化学成分软测量模型研究与应用[J]. 计算机测量与控制, 2009, 17(11): 2134. (SONG Qiang, WANG Ai-min. A study and application of soft measurement model of sinter chemical composition based on LS-SVM[J]. Computer Measurement and Control, 2009, 17(11): 2134.) [32] MENG J E, LIAO J, LIN J Y. Fuzzy neural networks-based quality prediction system for sintering process[J]. IEEE Transactions on Fuzzy Systems, 2000, 8(3): 314. [33] WANG H S, WANG Y N, WANG Y C. Cost estimation of plastic injection molding parts through integration of PSO and BP neural network[J]. Expert Systems with Applications, 2013, 40(2): 418. [34] DENG S J, JIA S Y, CHEN J. Exploring spatial-temporal relations via deep convolutional neural networks for traffic flow prediction with incomplete data[J]. Applied Soft Computing, 2019, 78: 712. [35] Kawaguchi T, Sato S, Takata K. Development and application of an integrated simulation model for iron ore sintering[J]. Iron and steel institute of japan, 1987, 73: 1940. [36] 袁晓丽, 范晓慧, 万新, 等. 烧结优化配矿模型的设计与软件开发[J]. 中南大学学报(自然科学版), 2009, 40(6):1476. (YUAN Xiao-li, FAN Xiao-hui, WAN Xin, et al. Design and software development of sintering optimized ore matching model[J]. Journal of Central South University (Natural Science), 2009, 40(6):1476.) [37] 张军红, 沈峰满, 谢安国. G-BP算法在烧结矿FeO指标预测中的应用[J]. 东北大学学报(自然科学版), 2002, 23(l1): 1073. (ZHANG Jun-hong, SHEN Feng-man, XIE An-guo. The application of G-BP algorithm in prediction of sinter FeO index[J]. Journal of Northeast University (Natural Science), 2002, 23(l1): 1073.) [38] CAO W C, ZHANG J L, ZHANG T, et al. A genetic algorithm application to minimize pig iron cost[J]. ISIJ International, 2013, 53(2): 207. [39] LI K J, ZHANG J L, LIU Z J, et al. Optimization model coupling both chemical compositions and high-temperature characteristics of sintering materials for sintering burden[J]. International Journal of Minerals, Metallurgy and Materials, 2014, 23(11): 216. [40] 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 study on ore blending technology of high-speed low-silicon sinter[J]. Iron and Steel, 2001, 36(12): 1.) [41] 张艳允. 邯钢烧结配料优化的探讨[J]. 烧结球团, 2002, 27(2): 8. (ZHANG Yan-yun. Discussion on optimization of sintering ingredients in Hangang[J]. Sintering Pellets, 2002, 27(2): 8.) [42] 王道群. 线性规划在烧结矿配料中的应用[J]. 中国冶金, 2005, 15(8): 19. (WANG Dao-qun. Application of linear programming in sinter blending[J]. China Metallurgical Industry, 2005, 15(8): 19.) [43] 吕学伟, 白晨光, 邱贵宝, 等. 基于遗传算法的烧结配料综合优化研究[J]. 钢铁, 2007, 42(4): 12. (L Xue-wei, BAI Chen-guang, QIU Gui-bao, et al. Research on comprehensive optimization of sintering ingredients based on genetic algorithm[J]. Iron and Steel, 2007, 42(4): 12.) [44] 王炜, 陈畏林, 贾斌, 等. 基于线性规划和神经网络的优化烧结配料系统开发[J]. 烧结球团, 2006, 31(1): 27. (WANG Wei, CHEN Wei-lin, JIA Bin, et al. Development of optimized sintering batching system based on linear programming and neural network [J]. Sintering Pellets, 2006, 31(1): 27.) [45] XU P, DU R, ZHANG Z B. Predicting pipeline leakage in petrochemical system through GAN and LSTM[J]. Knowledge-Based Systems, 2019, 175: 50. [46] DENG J F, SUN J, PENG W, et al. Application of neural networks for predicting hot-rolled strip crown[J]. Applied Soft Computing, 2019, 78: 119. [47] Ahmad M W, Reynolds J, Rezgui Y. Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees[J]. Journal of Cleaner Production, 2018, 203: 810. [48] LIU S, LYU Q, LIU X J, et al. A prediction system of burn through point based on gradient boosting decision tree and decision rules[J]. ISIJ International. 2019, 59(12): 2156. [49] LIU S, LIU X J, LYU Q, et al. Comprehensive prediction system of sinter composition based on DNN and LSTM[J]. Applied Soft Computing, 2020, 95: 106574. [50] LIU S, LÜ Q, LIU X J, et.al. Synthetically predicting the quality index of sinter using machine learning model[J]. Ironmaking and Steelmaking, 2020, 47(7): 828. [51] 殷瑞钰. 关于智能化钢厂的讨论——从物理系统一侧出发讨论钢厂智能化[J]. 钢铁, 2017, 52(6): 1. (YIN Rui-yu. Discussion on intelligent steel plant—Discussion on intelligent steel plant from physical system side[J]. Iron and Steel, 2017, 52(6): 1.) [52] HE R, CHEN G M, DONG C, et al. Data-driven digital twin technology for optimized control in process systems [J].ISA Transactions, 2019, 95: 221.
2021, Vol. 56 
