利用非数值型特征的转炉出钢合金化硅铁加入量预测模型开发  

Development of ferrosilicon addition prediction model for alloying at tapping in converter with non-numerical feature

作　　者：亢子成 钟良才 刘承军 于学渊 史秀成 赵阳 KANG Zicheng;ZHONG Liangcai;LIU Chengjun;YU Xueyuan;SHI Xiucheng;ZHAO Yang(School of Metallurgy,Northeastern University,Shenyang 110819,China;Steelmaking Plant of Fushun New Iron&Steel Company,Jianlong Group,Fushun 113001,China)

机构地区：[1]东北大学冶金学院,辽宁沈阳110819 [2]建龙集团抚顺新钢铁炼钢厂,辽宁抚顺113001

出　　处：《炼钢》2023年第6期23-29,共7页Steelmaking

摘　　要：利用100 t转炉出钢合金化数据,通过数据预处理和采用皮尔逊相关系数(Pearson correlation coefficient)进行特征选择,用非数值型变量—钢种作为其中的一个特征变量,采用SVR(Support Vector Regression,支持向量机回归)算法,建立出钢合金化硅铁加入量模型。引入钢种作为特征变量后建立的转炉出钢合金化硅铁加入量SVR模型,误差在±40 kg、±30 kg、±20 kg的范围下,预测的命中率分别为94.84%、87.58%、75.77%,而无钢种这一特征变量的SVR模型在相同的误差下的命中率分别为88.4%、80.61%、65.85%,表明采用钢种作为特征变量,提高了硅铁加入量预测模型准确度,对于实际出钢合金化具有更好的参考价值。By means of the alloying data of 100 t converter at tapping,a model for ferrosilicon addition was established by SVR(Support Vector Regression)algorithm through data preprocessing and the feature selection with Pearson correlation coefficient in this study,where steel grade,a non-numerical feature,was used as a feature variable.The hit rates of ferrosilicon addition prediction from the SVR model with steel grade as a feature variable are 94.84%,87.58%and 75.77%in the errors range of±40 kg,±30 kg and±20 kg,respectively,while those of the model without steel grade as a feature variable are 88.4%,80.61%and 65.85%under the same errors range,which proves that the model with steel grade as a feature variable has higher prediction accuracy,and has better reference value for actual alloying process.

关 键 词：转炉炼钢 出钢合金化 硅铁加入量 预测模型 非数值型特征 命中率 

分 类 号：TF711[冶金工程—钢铁冶金]