After the converter steelmaking process,a considerable number of ferroalloys are needed to remove dissolved oxygen from the molten steel,but it also forms a lot of oxide inclusions that cannot be completely removed.At...After the converter steelmaking process,a considerable number of ferroalloys are needed to remove dissolved oxygen from the molten steel,but it also forms a lot of oxide inclusions that cannot be completely removed.At the same time,it increases the carbon emis-sions in the steel production process.After years of research,our team have developed a series of clean deoxidation technologies,includ-ing carbon deoxidation,hydrogen deoxidation,and waste plastic deoxidation of molten steel to address the aforementioned issues.In this study,thermodynamic calculations and laboratory experiments were employed to verify that carbon and hydrogen can reduce the total oxygen content in the molten steel melt to below 5×10^(-6) and 10×10^(-6),respectively.An analysis of the deoxidation mechanisms and ef-fects of polyethylene and polypropylene was also conducted.In addition,the applications of carbon deoxidation technology in different steels with the hot-state experiment and industrial production were discussed carefully.The carbon deoxidation experimental results of different steels were as follows:(1)the oxygen content of bearing steel was effectively controlled at 6.3×10^(-6) and the inclusion number density was lowered by 74.73%compared to aluminum deoxidized bearing steel;(2)the oxygen content in gear steel was reduced to 7.7×10^(-6) and a 54.49%reduction of inclusion number density was achieved with almost no inclusions larger than 5μm from the average level of industry gear steels;(3)a total oxygen content of M2 high-speed steel was as low as 3.7×10^(-6).In industrial production practice,car-bon deoxidation technique was applied in the final deoxidation stage for non-aluminum deoxidized bearing steel,and it yielded excellent results that the oxygen content was reduced to below 8×10^(-6) and the oxide inclusions in the steel mainly consist of silicates,along with small amounts of spinel and calcium aluminate.展开更多
The steel industry,known for its complexity and the need to reduce CO_(2)emissions,is adopting advanced digitalization tools to move towards a more sustainable,integrated,and agile operating model.Digital twins with a...The steel industry,known for its complexity and the need to reduce CO_(2)emissions,is adopting advanced digitalization tools to move towards a more sustainable,integrated,and agile operating model.Digital twins with artificial intelligence-based optimization and scheduling models can improve decision-making in logistics,refractory maintenance,and energy efficiency.By incorporating advanced AI algorithms into this decision support system,the hot metal route scenarios can be evaluated,resulting in minimized hot metal temperature losses and increased scrap utilization.This paper integrated digital twins with reinforcement learning algorithms to investigate the logistics of torpedoes and hot metal ladles.It considered important input parameters such as the ladles and torpedoes'thermal state and location,refractory thickness,hot metal volume and temperature,and crane availability.By incorporating advanced AI algorithms into this decision support system,energy-efficient scenarios can be evaluated,increasing scrap utilization and resulting in a possible reduction of 15°C in hot metal temperature losses.展开更多
基金supported by the National Natural Science Foundation of China(No.52174297).
文摘After the converter steelmaking process,a considerable number of ferroalloys are needed to remove dissolved oxygen from the molten steel,but it also forms a lot of oxide inclusions that cannot be completely removed.At the same time,it increases the carbon emis-sions in the steel production process.After years of research,our team have developed a series of clean deoxidation technologies,includ-ing carbon deoxidation,hydrogen deoxidation,and waste plastic deoxidation of molten steel to address the aforementioned issues.In this study,thermodynamic calculations and laboratory experiments were employed to verify that carbon and hydrogen can reduce the total oxygen content in the molten steel melt to below 5×10^(-6) and 10×10^(-6),respectively.An analysis of the deoxidation mechanisms and ef-fects of polyethylene and polypropylene was also conducted.In addition,the applications of carbon deoxidation technology in different steels with the hot-state experiment and industrial production were discussed carefully.The carbon deoxidation experimental results of different steels were as follows:(1)the oxygen content of bearing steel was effectively controlled at 6.3×10^(-6) and the inclusion number density was lowered by 74.73%compared to aluminum deoxidized bearing steel;(2)the oxygen content in gear steel was reduced to 7.7×10^(-6) and a 54.49%reduction of inclusion number density was achieved with almost no inclusions larger than 5μm from the average level of industry gear steels;(3)a total oxygen content of M2 high-speed steel was as low as 3.7×10^(-6).In industrial production practice,car-bon deoxidation technique was applied in the final deoxidation stage for non-aluminum deoxidized bearing steel,and it yielded excellent results that the oxygen content was reduced to below 8×10^(-6) and the oxide inclusions in the steel mainly consist of silicates,along with small amounts of spinel and calcium aluminate.
文摘The steel industry,known for its complexity and the need to reduce CO_(2)emissions,is adopting advanced digitalization tools to move towards a more sustainable,integrated,and agile operating model.Digital twins with artificial intelligence-based optimization and scheduling models can improve decision-making in logistics,refractory maintenance,and energy efficiency.By incorporating advanced AI algorithms into this decision support system,the hot metal route scenarios can be evaluated,resulting in minimized hot metal temperature losses and increased scrap utilization.This paper integrated digital twins with reinforcement learning algorithms to investigate the logistics of torpedoes and hot metal ladles.It considered important input parameters such as the ladles and torpedoes'thermal state and location,refractory thickness,hot metal volume and temperature,and crane availability.By incorporating advanced AI algorithms into this decision support system,energy-efficient scenarios can be evaluated,increasing scrap utilization and resulting in a possible reduction of 15°C in hot metal temperature losses.
