高碳钢盘条冷却线FeO层厚百分比模型及其应用

特殊钢 ›› 2016, Vol. 37 ›› Issue (2): 8-11.

高碳钢盘条冷却线FeO层厚百分比模型及其应用

王煜¹，程晓茹¹，范敬国²，沈金龙²

1. 武汉科技大学钢铁冶金及资源利用省部共建教育部重点实验室，武汉 430081 ；
2. 武汉钢铁股份有限公司，武汉 430083

收稿日期:2015-10-26 出版日期:2016-04-01 发布日期:2022-08-05
通讯作者: 程晓茹
作者简介:王煜(1990-)，女，硕士生(武汉科技大学)，2012年长江大学(本科)毕业，高碳钢线材表面氧化铁皮研究。

Establishment of FeO Thickness Percentage Model of High Carbon Steel Coil at Cooling Line and Its Application

Wang Yu¹ , Cheng Xiaoru¹ , Fan Jingguo² , Shen Jinlong²

1. Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081 ;
2. Wuhan Iron and Steel Company Limited, Wuhan 430083

Received:2015-10-26 Published:2016-04-01 Online:2022-08-05
Contact: Cheng Xiaoru

摘要/Abstract

摘要： 72A高碳钢（0.67%~0.73%C）盘条的氧化铁皮总量和氧化铁皮中FeO层厚百分比显著影响盘条的剥离性能。利用人工神经网络和数理方法,建立BP网络模型,实现了高碳钢线材氧化过程中的冷却制度与氧化后生成的FeO层厚百分比之间的复杂的非线性映射关系。将实测的参数与网络模拟结果进行比较得出,建立的BP网络训练精度非常高,泛化能力强,能很好的反应各个因素对FeO层厚百分比的影响。生产应用结果表明,根据BP网络模拟结果改进冷却工艺,适当降低吐丝温度,提高850~720℃区间冷却速度,使氧化铁皮中FeO层厚百分比减小,改善了机械剥离性能。

关键词: 72A高碳钢盘条, 剥离性能, 人工神经网络, 冷却制度, FeO层厚百分比

Abstract: The iron-oxide scale total thickness and FeO layer thick percentage of 72A high carbon steel (0.67%-0.73% C) coils have markedly influence on coil stripping performance. With using artificial neural networks and mathematical method the BP network model is established to realize the complex non-linear mapping relationship between the cooling schemes during oxidation process of high carbon steel wire and the formed FeO layer thickness percentage after oxidation. Compared between measured parameters and network simulation results, it is obtained that the training accuracy of BP network is very high and has better generalization ability to quite well response the effect of each factor on FeO layer thickness percentage. The production application results show that based on BP network simulation results to improve cooling process, suitable decreasing wire laying-off temperature and increasing cooling speed at 850-720℃，the FeO layer thickness percentage of scale decreases in order to improve the mechanical peeling performance.

Key words: High Carbon Steel 72A Coil, Peeling Performance, Artificial Neural Network, Cooling Scheme, FeO Layer Thickness Percentage

王煜, 程晓茹, 范敬国, 沈金龙. 高碳钢盘条冷却线FeO层厚百分比模型及其应用[J]. 特殊钢, 2016, 37(2): 8-11.

Wang Yu , Cheng Xiaoru , Fan Jingguo , Shen Jinlong. Establishment of FeO Thickness Percentage Model of High Carbon Steel Coil at Cooling Line and Its Application[J]. Special Steel, 2016, 37(2): 8-11.

[1]	于学森, 彭其春, 童志博, 曹建新, 刘红军, 周春泉, 杨秀枝, 刘文华. 减少A36含硼钢板坯角部横裂纹的工艺实践[J]. 特殊钢, 2012, 33(6): 29-31.
[2]	郑忠, 史战东, 高小强, 代有训. 两种转炉炼钢终点控制模型的比较[J]. 特殊钢, 2008, 29(6): 20-22.
[3]	刘平李建宣何勇岳彤彪. 人工神经网络模型补偿Consteel电弧炉钢水称重系统的测算偏差[J]. 特殊钢, 2008, 29(2): 53-55.
[4]	程晓茹, 任勇, 胡衍生, 江定辉, 刘建斌. 热轧钢板头部弯曲行为的人工神经网络预报模型[J]. 特殊钢, 2007, 28(2): 1-3.