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
Splashing behavior of metal droplets is one of the main phenomena in basic oxygen furnace steelmaking process.The size distribution of metal droplets and the residence time of the metal droplets in the slag have impor...Splashing behavior of metal droplets is one of the main phenomena in basic oxygen furnace steelmaking process.The size distribution of metal droplets and the residence time of the metal droplets in the slag have important effects on the kinetics of the metal–slag reactions.The particle size distribution law,characteristic diameter,splashing velocity and splashing angle of metal droplets were investigated,and an improved prediction model of trajectory and residence time for metal droplets was established based on the combination of expanded droplets theory,decarburization mechanism model and ballistic motion principle.Meanwhile,the trajectory and residence time of metal droplets under different working conditions were analyzed based on this model.The results illustrate that the metal droplets with larger particle size are produced at low lance distance,while the metal droplets with smaller particle size are produced at high lance distance.There is a significant linear relationship between the three diameters(maximum droplet diameter,distribution characteristic diameter,reaction characteristic diameter)and the blowing number.The residence time of decarbonized metal droplets in slag is about 0.2–73 s.Meanwhile,the initial carbon content and diameter of the metal droplets and the FeO content of slag are the main factors affecting the motion state of the metal droplets in the slag,while the splashing velocity,splashing angle and the height of the foam slag have little influence.This model can be used to predict the trajectory and residence time of decarburized metal droplets in a variety of complex multiphase slag conditions,overcoming the limitation that the known model is only applicable to a few specific conditions.展开更多
采用文献计量方法,对2000～2009年Web of ScienceSCIE数据库收录湿地研究的文献进行分析,探讨了湿地研究的年代分布、期刊分布、语种、文献类型、学科分布及排名前10位的国家和机构,以期了解世界各国在这一研究领域的进展情况,并为专业...采用文献计量方法,对2000～2009年Web of ScienceSCIE数据库收录湿地研究的文献进行分析,探讨了湿地研究的年代分布、期刊分布、语种、文献类型、学科分布及排名前10位的国家和机构,以期了解世界各国在这一研究领域的进展情况,并为专业研究人员提供信息参考。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funded by the National Natural Science Foundation of China(Nos.52374321 and 51974023)the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing(No.41621005)the Youth Science and Technology Innovation Fund of Jianlong Group-University of Science and Technology Beijing(No.20231235).
文摘Splashing behavior of metal droplets is one of the main phenomena in basic oxygen furnace steelmaking process.The size distribution of metal droplets and the residence time of the metal droplets in the slag have important effects on the kinetics of the metal–slag reactions.The particle size distribution law,characteristic diameter,splashing velocity and splashing angle of metal droplets were investigated,and an improved prediction model of trajectory and residence time for metal droplets was established based on the combination of expanded droplets theory,decarburization mechanism model and ballistic motion principle.Meanwhile,the trajectory and residence time of metal droplets under different working conditions were analyzed based on this model.The results illustrate that the metal droplets with larger particle size are produced at low lance distance,while the metal droplets with smaller particle size are produced at high lance distance.There is a significant linear relationship between the three diameters(maximum droplet diameter,distribution characteristic diameter,reaction characteristic diameter)and the blowing number.The residence time of decarbonized metal droplets in slag is about 0.2–73 s.Meanwhile,the initial carbon content and diameter of the metal droplets and the FeO content of slag are the main factors affecting the motion state of the metal droplets in the slag,while the splashing velocity,splashing angle and the height of the foam slag have little influence.This model can be used to predict the trajectory and residence time of decarburized metal droplets in a variety of complex multiphase slag conditions,overcoming the limitation that the known model is only applicable to a few specific conditions.
