摘要
在高炉炼铁过程中,常用铁水硅含量[Si]来衡量铁水的质量和表征高炉的热状态,即用铁水硅含量反映高炉炉温.将偏最小二乘回归方法应用于预测硅含量[Si]中,在高炉炉况相对稳定的条件下,得出影响硅含量[Si]的因素为风量和喷煤,与冶炼专家的经验相符.利用包钢6号高炉的数据,建立铁水硅含量[Si]的回归模型,该模型对高炉炉温预测的准确度达到87.61%,对在线监测高炉硅含量具有一定的实用价值.
In blast furnace ironmaking process,the silicon content in hot metal is usually used to measure the quality of hot metal and reflect the thermal state of blast furnace.The partial least square regression method was used to predict the silicon content in hot metal.Under the situation of blast furnace relatively stable,it was obtained that the factors impacting the silicon content were air flow and coal injection.This result conformed to the experience of the experts.Based on the data from No.6 blast furnace of Baotou Steel,a regression model of silicon content in hot metal was established.It can provide an accuracy of 87.61% in prediction of temperature in blast furnace and has some practical use in online monitoring the silicon content in hot metal.
出处
《内蒙古大学学报（自然科学版）》
CAS
CSCD
北大核心
2010年第4期427-430,共4页
Journal of Inner Mongolia University：Natural Science Edition
基金
教育部"春晖计划"合作项目(Z2009-1-01053)
内蒙古教育厅研究基金资助项目(NJzy08075)
关键词
高炉冶炼
铁水硅含量
偏最小二乘
炉温预测
blast furnace ironmaking hot metal silicon content partial least square temperature prediction
