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| Mechanism and forecast model of sticker breakout in continuous casting |
| WANG Rongrong1,2, WANG Min3,4, WANG Zhongliang3, XING Lidong3,4, AI Xingang5, BAO Yanping3 |
1. School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. HBIS Materials Technology Research Institute, Shijiazhuang 052165, Hebei, China;
3. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China;
4. Technical Support Center for Prevention and Control of Disastrous Accidents in Metal Smelting, University of Science and Technology Beijing, Beijing 100083, China;
5. School of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan 114051, Liaoning, China |
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Abstract Continuous casting is one of the most important processes in the steel making process. There are many steel leakage accidents in the continuous casting process, which have many influencing factors and complex mechanisms. Among them, sticker breakout is the most common accident, accounting for about 70% of the total steel leakage. Continuous casting leakage can easily cause steel leakage, burns, fires and even explosions, resulting in casualties and huge property losses. In view of the above difficulties, the expansion of sticker fracture and the formation mechanism of sticker breakout were analyzed, the principle of forecasting sticker breakout was proposed based on thermocouple temperature measurement method and a sticker breakout forecasting model was established using neural network. The correct reporting rate of the test samples for sticker breakout forecasting model reached 100%, the forecasting rate was 97.56%. The spatial network model was verified, the output of the A-type spatial network met the expectation and could achieve the prediction of bonding in the spatial fracture expansion. The model has good application value and can provide support for the safe production of continuous casting.
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Received: 05 June 2023
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| [1] |
高宇,张燕平,张士慧,等. 结晶器漏钢预报专家系统的开发与应用[J]. 河北冶金,2020(1):20.
|
| [2] |
徐西平. 连铸机漏钢预报系统应用和开发研究[J]. 科技风,2019(2):70.
|
| [3] |
姚思源. 板坯连铸漏钢的影响因素分析及控制措施[J]. 科技展望,2015,25(24):58.
|
| [4] |
黎建全,龙木军,陈登富,等. 板坯连铸机二次冷却均匀性的分析与优化[J]. 炼钢,2021,37(1):57.
|
| [5] |
LIU Y,LEI Z K,ZHU R X,et al. Artificial neural network prediction of residual compressive strength of composite stiffened panels with open crack[J]. Ocean Engineering,2022,266(1):112771.
|
| [6] |
戴智才,罗钢,肖磊,等. 薄板坯连铸连轧生产线75Cr1连铸工艺优化[J]. 金属材料与冶金工程,2022,50(6):47.
|
| [7] |
ANSARI M O,GHOSE J,GHOSH D,et al. An intelligent logic-based mold breakout prediction system algorithm for the continuous casting process of steel: A novel study[J]. Micromachines,2022,13(12):2148.
|
| [8] |
程晓,巩彦坤. 板坯铸机角部漏钢事故原因分析及预防[J]. 甘肃冶金,2022,44(4):43.
|
| [9] |
冯赞,廖宏义,脱臣德,等. 炼钢连铸工艺对低合金钢特厚板Z向性能影响[J]. 金属材料与冶金工程,2021,49(5):27.
|
| [10] |
YUAN G,WANG F L,GUO Y W,et al. Study of BP-Adaboost algorithm for diagnosing aircraft cable faults[C]//2022第34届中国控制与决策会议.合肥:东北大学,中国自动化学会信息物理系统控制与决策专业委员会,2022:6.
|
| [11] |
NEUNER M,ABRARI V S,ARUNACHALA P K,et al. A better understanding of the mechanics of borehole breakout utilizing a finite strain gradient-enhanced micropolar continuum model[J]. Computers and Geotechnics,2023,153:105064.
|
| [12] |
王利国,魏合意. 钢包下水口漏钢原因分析及改进措施[J]. 耐火与石灰,2023,48(1):21.
|
| [13] |
吴迪等,张本国,生万宝,等. 基于差分进化-灰狼优化算法的支持向量机连铸漏钢预报系统研究[J]. 特种铸造及有色合金,2023,43(2):174.
|
| [14] |
ZHANG B G,SHEGN W B,WU D,et al., Application of GA-ACO algorithm in thin slab continuous casting breakout prediction[J]. Transactions of the Indian Institute of Metals,2022,76(1):145.
|
| [15] |
秦梦泽,张勇. 基于热-力信息融合的智能漏钢预报技术研究[J]. 炼钢,2020,36(4):65.
|
| [16] |
TIAN Y P,LIU Y. Intelligent breakout prediction method based on support vector machine[J]. Journal of Physics: Conference Series,2020(1):012052.
|
| [17] |
郑贺军. 连铸漏钢预报研究及在连铸结晶器振动监控软件中的实现[D]. 秦皇岛:燕山大学,2017.
|
| [18] |
黄回亮,曾令宇,杨帆. 宽板坯连铸机粘结现象分析[C]//2007年度泛珠三角十一省(区)炼钢连铸年会.柳州:广西柳州钢铁(集团)公司,2007:4.
|
| [19] |
ZHANG Y,LI P. A Study of privacy-preserving neural network prediction based on replicated secret sharing[J]. Mathematics,2023,11(4):1048.
|
| [20] |
WANG Y Y,WANG X D,YAO M. Integrated model of ACWGAN-GP and computer vision for breakout prediction in continuous casting[J]. Metallurgical and Materials Transactions B,2022,53(5):2873.
|
| [21] |
LO B V,GUARINO S,BUSCEMI A,et al., Development of neural network prediction models for the energy producibility of a parabolic dish: A comparison with the analytical approach[J]. Energies,2022,15(24):9298.
|
| [22] |
生万宝,张本国,吴迪,等. 基于蚁群算法-BP神经网络的漏钢预报模型研究[J]. 特种铸造及有色合金,2022,42(11):1366.
|
| [23] |
徐海伦,马春武,李清忠,等. 结晶器漏钢过程解析及预报原理[J]. 钢铁钒钛,2012,33(5):35.
|
| [24] |
王红军,许颖敏,叶飞. 武钢CSP连铸生产SPA-H铸中黏连漏钢分析[C]//第十三届中国钢铁年会. 重庆:中国金属学会,2022:7.
|
| [25] |
李继,陈守杰,王勇源,等. 小板坯粘结漏钢原因分析及控制措施[J]. 河北冶金,2022(4):51.
|
| [26] |
CHEN D,CHEN C X,ZHOU C H. System effectiveness evaluation method based on GA-BP under the condition of small sample[C]//第41届中国控制会议. 合肥:中国自动化学会控制理论专业委员会,2022:6.
|
| [27] |
GUO L H,YANG L M,PENG Y F,et al. Fault identification of low-speed hub bearing of crane based on MBMD and BP neural network[J]. Shock and Vibration,2022(4):1924.
|
| [28] |
LING X,LIU S P,LIU Q,et al. Research on remaining service life prediction of platform screen doors system based on genetic algorithm to optimise BP neural network[J]. Enterprise Information Systems,2022,16(8/9):1526.
|
| [29] |
李楠,孙伯鑫,焦庆宇,等. 基于GA-BP神经网络的终端区航迹预测[C]//世界交通运输工程技术论坛(WTC2021). 西安:中国公路学会,2021:10.
|
| [30] |
ZHOU Y J,YU X L,WANG D H,et al. Optimization analysis of distribution of RFID multitag based on GA-BP neural network[C]// IEEE第二届先进信息技术、电子和汽车展览会论文集. 重庆:IEEE BEIJING SECTION,2017:5.
|
| [31] |
JI C,CAI Z Z,TAO N B,Molten steel breakout prediction based on genetic algorithm and BP neural network in continuous casting process[C]//第三十一届中国控制会议.合肥:中国自动化学会控制理论专业委员会,2012:5.
|
| [32] |
王利霞,蔡金锭,林礼清. 基于GA-BP神经网络的故障诊断专家系统在水轮机组中的应用[C]//中国电力系统保护与控制学术研讨会. 珠海:许昌继电器研究所《电力系统保护与控制》杂志社,2006:4.
|
| [33] |
周荣宝,王寅,陈鹏飞,等. 高炉综合炉料熔滴性能及其预测模型[J]. 中国冶金,2023,33(8):33.
|
| [34] |
刘宇,王旭东,杜凤鸣,等. 连铸板坯黏结漏钢可视化检测方法[C]//高品质钢连铸生产技术及装备交流会. 长沙:中国金属学会,2014:7.
|
| [35] |
罗庆梅,商晓东. 漏钢预报系统软硬件优化[C]//全国冶金自动化信息网年会. 北京:《冶金自动化》杂志社,2014:4.
|
| [36] |
田陆,刘晓玲. 神经网络在漏钢预报中的应用[C]//中国计量协会冶金分会. 杭州:中国金属学会,2010:4.
|
| [37] |
段海洋,王旭东,姚曼. 基于温度时序特征层次聚类的连铸黏结漏钢预报方法开发[J]. 机械工程学报,2020,56(8):250.
|
| [38] |
宁伟. 降低厚板坯连铸机结晶器漏钢预报专家系统误报率的技术研究与应用[J]. 宽厚板,2020,26(5):26.
|
| [39] |
刘怡,岳洪亮,陈其国. 基于多机制判断的板坯连铸漏钢预报系统研究[J]. 冶金自动化,2019,43(6):47.
|
| [40] |
刘宇,王旭东,姚曼,等. 连铸结晶器黏结漏钢的可视化及其识别方法[J]. 钢铁研究学报,2015:27(7):37.
|
| [41] |
崔拓. 连铸板坯SS400钢黏结漏钢攻关实践[J]. 连铸,2015(6):67.
|
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|
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2023, Vol. 33 
