摘要
为了给高炉提供合格的烧结矿,提出基于烧结生产线各个环节的大量数据,将XGBoost算法、因子相关分析与深度学习算法相结合的大数据技术对烧结矿小于10mm粒级含量进行预测。首先,对烧结厂数据库的数据进行搜集、整合和预处理;其次,进行因子分析,筛选出适合建模的14个相关变量并进行变量之间的相关性分析;最后,建立深度神经网络算法模型。通过测试并与传统算法模型进行性能比较,结果表明,模型预测效果很好,达到了精确预测烧结矿小于10mm粒级含量的目的,对烧结实际生产具有很好的指导意义。
In order to provide qualified sinter for the blast furnace,a large data method combining XGBoost algorithm,factor correlation analysis and deep learning algorithm was proposed to predict the sinter content of less than 10 mm based on a large amount of data in each part of the sintering production.Firstly,the data from the sinter plant database need to be collected,integrated and preprocessed.Secondly,the factor analysis was carried out,to select 14 relevant variables suitable for model training and to perform correlation analysis between variables.Finally,the deep neural network algorithm model was established.By testing the model and comparing with the traditional algorithm model,the results showed that the model prediction effect was very good,and the purpose of accurately predicting the content of sinter particle size less than 10 mm was achieved,which had a good guiding significance for the actual production of sintering.
作者
刘月明
刘小杰
吕庆
张振峰
刘颂
刘福龙
LIU Yue-ming;LIU Xiao-jie;LU Qing;ZHANG Zhen-feng;LIU Song;LIU Fu-long(School of Metallurgy and Energy,North China University of Science and Technology,Tangshan 063009,Hebei,China;Chengde Company,Hesteel Group,Chengde 067000,Hebei,China;Central Iron and Steel Research Institute,Hesteel Group,Shijiazhuang 050023,Hebei,China)
出处
《中国冶金》
CAS
北大核心
2019年第11期31-38,共8页
China Metallurgy
基金
河北省研究生创新资助项目(2017B03)
