To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta ...To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta criterion, hierarchical clustering, and principal component analysis on the original data were performed. Secondly, the prediction results of classic machine learning models of ridge regression, support vector machine, gradient boosting regression (GBR), random forest regression, back-propagation (BP) neural network models, and multi-layer perceptron (MLP) were compared before and after data preprocessing. An improved model was established based on the improved sparrow algorithm and BP using tent chaotic mapping (CSSA-BP). The CSSA-BP model showed the best performance for endpoint carbon prediction with the lowest mean absolute error (MAE) and root mean square error (RMSE) values of 0.01124 and 0.01345 mass% among seven models, respectively. And the lowest MAE and RMSE values of 8.9839 and 10.9321 ℃ for endpoint temperature prediction were obtained among seven models, respectively. Furthermore, the CSSA-BP and GBR models have the smallest error fluctuation range in both endpoint carbon content and temperature predictions. Finally, in order to improve the interpretability of the model, SHapley additive interpretation (SHAP) was used to analyze the results.展开更多
A 1∶8 physical water model was constructed to investigate the fluid flow and mixing phenomena in the basic oxygen furnace(BOF)converter.The particle image velocimetry was employed to measure the velocity distribution...A 1∶8 physical water model was constructed to investigate the fluid flow and mixing phenomena in the basic oxygen furnace(BOF)converter.The particle image velocimetry was employed to measure the velocity distribution of the bath and the high-speed camera was applied to capture the cavity shape in the combined blowing BOF converter.The mixing time for varied operating conditions was measured by the stimulus-response approach.The cavity depth increased with the decrease in the lance height and the increase in the top gas flow rate while the bottom blowing gas had little influence on the cavity depth.The minimum cavity depth was obtained under the condition of a 69.8 m^(3)/h top gas flow rate,a 287.5 mm lance height and a 0.93 m^(3)/h bottom blowing gas flow rate,which was 161.2 mm.The mixing time decreased as the lance height decreased and the top blowing gas flow rate increased.The mixing time was first decreased and then increased with the increase in the bottom gas flow rate.With the condition of 69.8 m^(3)/h gas flow rate of top blowing,the 287.5 mm lance height and the 0.93 m^(3)/h gas flow rate of bottom blowing,the mixing time in the converter was 48.65 s.The empirical formula between the stirring power and the mixing time in the converter was calculated.展开更多
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...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 prediction values were proposed.展开更多
The 18 crude drugs in Bofutsushosan (BOF: Pulvis ledebouriellae compositae: 防風通聖散) are separated into 6 groups such as diaphoretic, cathartic, antidote, antipyretic, neutralizer and diuretic groups. The effects o...The 18 crude drugs in Bofutsushosan (BOF: Pulvis ledebouriellae compositae: 防風通聖散) are separated into 6 groups such as diaphoretic, cathartic, antidote, antipyretic, neutralizer and diuretic groups. The effects of single administered BOF and composed crude drugs in 6 groups were investigated on the levels of diabetic parameters (serum glucose, insulin, triglyceride and cholesterol) in streptozotocin-induced diabetic mice. The anti-hyperglycemic action of BOF was depended on Ephedrae Herba, Saposhnikoviae Radix and Schizonepetae Spica in diaphoretic group, Forsythiae Fructus, Saposhnikoviae Radix, Schizonepetae Spica and Cnidii Rhizoma in antidote group, Scutellariae Radix, Gardeniae Fructus and Gypsum Fibrosum in antipyretic group and Paeoniae Radix in neutralizer group. In these crude drugs, Ephedrae Herba, Saposhnikoviae Radix, Schizonepetae Spica, Forsythiae Fructus, Scutellariae Radix, Gypsum Fibrosum and Paeoniae Radix increased serum insulin level, but Cnidii Rhizoma and Gardeniae Fructus did not affect serum insulin level. From these results, it suggested that anti-hyperglycemic action of BOF was through insulin-dependent and insulin independent manners. The lowering effect of BOF on serum triglyceride level was dependent on actions of Platycodi Radix in antidote and diuretic groups and Gardeniae Fructus in antipyretic group. The lowering effect of Gardeniae Fructus was parallel with its anti-hyperglycemic action. The lowering effect of BOF on high serum triglyceride level also included both direct action and indirect action. The reducing effect of BOF on serum cholesterol level was observed together with the actions of Ephedrae Herba and Zingiberis Rhizoma in diaphoretic group, Schizonepetae Spica in diaphoretic and antidote groups and Paeoniae Radix in neutralizer group. The lowering effects of Ephedrae Herba, Schizonepetae Spica and Paeoniae Radix on serum cholesterol level were parallel with their anti-hyperglycemic actions. Zingiberis Rhizoma in diaphoretic group might be direct reducing effect on serum cholesterol level but no serum glucose level. The Ephedrae Herba in diaphoretic group, Schizonepetae Spica in diaphoretic and antidote groups and Paeoniae Radix in neutralizer group might have reduced serum cholesterol level by reducing blood glucose level. From these results, composed crude drugs in 6 groups show various mechanisms in the action of BOF.展开更多
石油套管用钢(/%:0.26~0.29C,0.25~0.35Si,0.40~0.50Mn,≤0.009P,≤0.004S,0.95~1.05Cr,0.09~0.11V,0.02~0.04Al,0.015~0.020Ti,≤0.006 0N)的生产流程为铁水预处理-120 t BOF-吹氩-LF-喂CaSi线-RH-合金化-喂CaSi线-软吹氩-Φ220 mm圆...石油套管用钢(/%:0.26~0.29C,0.25~0.35Si,0.40~0.50Mn,≤0.009P,≤0.004S,0.95~1.05Cr,0.09~0.11V,0.02~0.04Al,0.015~0.020Ti,≤0.006 0N)的生产流程为铁水预处理-120 t BOF-吹氩-LF-喂CaSi线-RH-合金化-喂CaSi线-软吹氩-Φ220 mm圆坯连铸工艺。通过热力学分析得出钢中N含量超过50×10^(-6)以及工业试验得出生产的圆铸坯中的N含量为67×10^(-6)时,在铸坯中易形成2μm以上的TiN夹杂。通过控制BOF终点[N]≤30×10^(-6),LF终点[S]≤25×10^(-6),[O]≤25×10^(-6),[N]≤35×10^(-6),RH合金化后终点[N]≤35×10^(-6),[H]≤1.5×10^(-6),稳定喂CaSi线速度300~400 m/min,控制中间包[N]≤40×10^(-6),严格连铸保护浇铸工艺,则铸坯中的N含量≤50×10^(-6),钢中TiN夹杂数量显著下降,未发现大尺寸TiN夹杂物。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.U1960202)the Science and Technology Commission of Shanghai Municipality(No.19DZ2270200).
文摘To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta criterion, hierarchical clustering, and principal component analysis on the original data were performed. Secondly, the prediction results of classic machine learning models of ridge regression, support vector machine, gradient boosting regression (GBR), random forest regression, back-propagation (BP) neural network models, and multi-layer perceptron (MLP) were compared before and after data preprocessing. An improved model was established based on the improved sparrow algorithm and BP using tent chaotic mapping (CSSA-BP). The CSSA-BP model showed the best performance for endpoint carbon prediction with the lowest mean absolute error (MAE) and root mean square error (RMSE) values of 0.01124 and 0.01345 mass% among seven models, respectively. And the lowest MAE and RMSE values of 8.9839 and 10.9321 ℃ for endpoint temperature prediction were obtained among seven models, respectively. Furthermore, the CSSA-BP and GBR models have the smallest error fluctuation range in both endpoint carbon content and temperature predictions. Finally, in order to improve the interpretability of the model, SHapley additive interpretation (SHAP) was used to analyze the results.
基金support from the National Natural Science Foundation of China(U22A20171)the High Steel Center(HSC)at North China University of Technology and University of Science and Technology Beijing,China.
文摘A 1∶8 physical water model was constructed to investigate the fluid flow and mixing phenomena in the basic oxygen furnace(BOF)converter.The particle image velocimetry was employed to measure the velocity distribution of the bath and the high-speed camera was applied to capture the cavity shape in the combined blowing BOF converter.The mixing time for varied operating conditions was measured by the stimulus-response approach.The cavity depth increased with the decrease in the lance height and the increase in the top gas flow rate while the bottom blowing gas had little influence on the cavity depth.The minimum cavity depth was obtained under the condition of a 69.8 m^(3)/h top gas flow rate,a 287.5 mm lance height and a 0.93 m^(3)/h bottom blowing gas flow rate,which was 161.2 mm.The mixing time decreased as the lance height decreased and the top blowing gas flow rate increased.The mixing time was first decreased and then increased with the increase in the bottom gas flow rate.With the condition of 69.8 m^(3)/h gas flow rate of top blowing,the 287.5 mm lance height and the 0.93 m^(3)/h gas flow rate of bottom blowing,the mixing time in the converter was 48.65 s.The empirical formula between the stirring power and the mixing time in the converter was calculated.
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.
文摘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 prediction values were proposed.
文摘The 18 crude drugs in Bofutsushosan (BOF: Pulvis ledebouriellae compositae: 防風通聖散) are separated into 6 groups such as diaphoretic, cathartic, antidote, antipyretic, neutralizer and diuretic groups. The effects of single administered BOF and composed crude drugs in 6 groups were investigated on the levels of diabetic parameters (serum glucose, insulin, triglyceride and cholesterol) in streptozotocin-induced diabetic mice. The anti-hyperglycemic action of BOF was depended on Ephedrae Herba, Saposhnikoviae Radix and Schizonepetae Spica in diaphoretic group, Forsythiae Fructus, Saposhnikoviae Radix, Schizonepetae Spica and Cnidii Rhizoma in antidote group, Scutellariae Radix, Gardeniae Fructus and Gypsum Fibrosum in antipyretic group and Paeoniae Radix in neutralizer group. In these crude drugs, Ephedrae Herba, Saposhnikoviae Radix, Schizonepetae Spica, Forsythiae Fructus, Scutellariae Radix, Gypsum Fibrosum and Paeoniae Radix increased serum insulin level, but Cnidii Rhizoma and Gardeniae Fructus did not affect serum insulin level. From these results, it suggested that anti-hyperglycemic action of BOF was through insulin-dependent and insulin independent manners. The lowering effect of BOF on serum triglyceride level was dependent on actions of Platycodi Radix in antidote and diuretic groups and Gardeniae Fructus in antipyretic group. The lowering effect of Gardeniae Fructus was parallel with its anti-hyperglycemic action. The lowering effect of BOF on high serum triglyceride level also included both direct action and indirect action. The reducing effect of BOF on serum cholesterol level was observed together with the actions of Ephedrae Herba and Zingiberis Rhizoma in diaphoretic group, Schizonepetae Spica in diaphoretic and antidote groups and Paeoniae Radix in neutralizer group. The lowering effects of Ephedrae Herba, Schizonepetae Spica and Paeoniae Radix on serum cholesterol level were parallel with their anti-hyperglycemic actions. Zingiberis Rhizoma in diaphoretic group might be direct reducing effect on serum cholesterol level but no serum glucose level. The Ephedrae Herba in diaphoretic group, Schizonepetae Spica in diaphoretic and antidote groups and Paeoniae Radix in neutralizer group might have reduced serum cholesterol level by reducing blood glucose level. From these results, composed crude drugs in 6 groups show various mechanisms in the action of BOF.
