TSC prediction and dynamic control of BOF steelmaking with state-of-the-art machine learning and deep learning methods  

作　　者：Tian-yi Xie Cai-dong Zhang Quan-lin Zhou Zhi-qiang Tian Shuai Liu Han-jie Guo 

机构地区：[1]Material Technology Research Institute,Hesteel Group,Shijiazhuang,050023,Hebei,China [2]Tangsteel Company,Hesteel Group,Tangshan,063611,Hebei,China [3]School of Artificial Intelligence,Beijing Technology and Business University,Beijing,100048,China [4]School of Metallurgical and Ecological Engineering,University of Science and Technology Beijing,Beijing,100083,China

出　　处：《Journal of Iron and Steel Research International》2024年第1期174-194,共21页

基　　金：This research has been supported by the Natural Science Foundation of Hebei Province,China(E2022318002).Thanks are given to Tangsteel Co.,Ltd.of Hesteel Group and Digital Co.,Ltd.of Hesteel Group for providing detailed data,hardware and software support for model development and field production test.

摘　　要：Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC test.To maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the field.The number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as inputs.First,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2)values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time series.Through optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2)values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,respectively.Its field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carried out,and dynamic control methods based on pred

关 键 词：BOF steelmaking In-blow prediction TSC test Machine learning Deep learning Field application 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]