A 3D mathematical model was established to investigate the gas-liquid two-phase flow in Ruhrstahl-Heraeus(RH)vacuum refining process.The flow characteristics of molten steel were calculated using the coupled standard...A 3D mathematical model was established to investigate the gas-liquid two-phase flow in Ruhrstahl-Heraeus(RH)vacuum refining process.The flow characteristics of molten steel were calculated using the coupled standard k-εmodel and volume of fluid model.The bubble distribution was tracked by discrete phase model.Electromagnetic field was applied in the up-leg snorkel to enhance the effect of vacuum refining.The effect of swirling flow nozzles combined with electromagnetic stirring(EMS)on the flow characteristics of molten steel and bubble distribution was analyzed.The erosion of the up-leg snorkel was compared.The results show that when the swirling flow nozzles are used,the bubbles exhibit a distinct adherent rising behavior,and the refining efficiency decreases.In addition,the electromagnetic field can significantly improve the refining efficiency,but it brings stronger erosion to the up-leg snorkel.Nevertheless,when using the swirling flow nozzles combined with EMS,the refining performance is further optimized,and the erosion of the up-leg snorkel is also reduced due to its characteristic of bubble distribution.Compared to conventional nozzles,the mixing time was shortened by 16.2%,the recirculation rate increased by 12.5%.and the swirling intensity was strengthened by 8.9%.展开更多
The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on e...The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.展开更多
基金support from the National Natural Science Foundation of China(No.52174305).
文摘A 3D mathematical model was established to investigate the gas-liquid two-phase flow in Ruhrstahl-Heraeus(RH)vacuum refining process.The flow characteristics of molten steel were calculated using the coupled standard k-εmodel and volume of fluid model.The bubble distribution was tracked by discrete phase model.Electromagnetic field was applied in the up-leg snorkel to enhance the effect of vacuum refining.The effect of swirling flow nozzles combined with electromagnetic stirring(EMS)on the flow characteristics of molten steel and bubble distribution was analyzed.The erosion of the up-leg snorkel was compared.The results show that when the swirling flow nozzles are used,the bubbles exhibit a distinct adherent rising behavior,and the refining efficiency decreases.In addition,the electromagnetic field can significantly improve the refining efficiency,but it brings stronger erosion to the up-leg snorkel.Nevertheless,when using the swirling flow nozzles combined with EMS,the refining performance is further optimized,and the erosion of the up-leg snorkel is also reduced due to its characteristic of bubble distribution.Compared to conventional nozzles,the mixing time was shortened by 16.2%,the recirculation rate increased by 12.5%.and the swirling intensity was strengthened by 8.9%.
基金supported by the National Key Research and Development Program of China(No.2023YFB3712401),the National Natural Science Foundation of China(No.52274301)the Aeronautical Science Foundation of China(No.2023Z0530S6005)the Ningbo Yongjiang Talent-Introduction Programme(No.2022A-023-C).
文摘The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.
