连铸结晶器粘结性漏钢预报系统研究与设计
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摘要
介绍了粘结性漏钢的形成过程,对比分析了正常工况和粘结漏钢形成过程中结晶器壁的温度变化特征。通过BP神经网络建立了漏钢预报温度识别模型,用某钢厂200组典型历史温度数据对其进行训练;采用虚拟仪器平台搭建了漏钢预报实验系统并进行了模拟实验。结果表明,该方法预报实时、准确,具有一定的应用价值。
The forming process of sticking breakout was introduced, and the temperature variation characteristics of mold wall in normal working condition and bonding process were compared and analyzed. BP neural network was used to establish the temperature identification model for molten steel leakage prediction. 200 groups of typical historical temperature data of a steel plant were used for training. The simulation experiment was carried out by using the virtual instrument platform to build the molten steel leakage prediction experiment system. The results show that the method is practical, accurate and certain application value.
The forming process of sticking breakout was introduced, and the temperature variation characteristics of mold wall in normal working condition and bonding process were compared and analyzed. BP neural network was used to establish the temperature identification model for molten steel leakage prediction. 200 groups of typical historical temperature data of a steel plant were used for training. The simulation experiment was carried out by using the virtual instrument platform to build the molten steel leakage prediction experiment system. The results show that the method is practical, accurate and certain application value.
引文
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[2]肖俊生,任祎龙,李文涛.基于粒子群算法优化BP神经网络漏钢预报的研究[J].计算机测量与控制,2015,23(4):1302-1304.
[3]何飞,张玲颖,谢西安,等.连铸结晶器内凝固坯壳粘结行为研究现状[J].钢铁研究学报,2017,29(8):603-609.
[4]何飞,吴鹏飞,徐其言,等.连铸粘结漏钢的结晶器温度变化及其传播行为[J].钢铁研究学报,2016,28(2):27-32.
[5]李文涛,李杨.基于虚拟仪器的结晶器漏钢预报系统研究[J].工矿自动化,2010,36(6):81-85.
[6]王春梅,孙丽凤,张本国,等.基于遗传算法优化BP神经网络漏钢预报的研究[J].铸造技术,2014,35(4):736-739.
[7]徐海伦,马春成,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):35-40.
[8]史聚.板坯连铸漏钢预报系统的研究[J].重庆科技学院学报(自然科学版),2008(1):66-68.
[9]刘永贞,王旭东,贾启忠,等.结晶器内粘结漏钢及其传播行为的研究[J].炼钢,2009,25(3):45-48,52.
[10]肖俊生,周小凤,左鸿飞.基于LabVIEW和PXI的某装甲车仪表系统设计[J].制造业自动化,2013,35(17):150-153.