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
为了提高IF钢的洁净度及可浇性,结合邯钢公司邯宝炼钢厂260 t RH真空炉生产实际,以16炉H-OLTB转炉工艺试验为样本,通过取样、夹杂物扫描电镜和数据分析等手段,对冶炼IF钢时自由脱碳模式和强制脱碳模式的终点碳含量、脱碳时间、顶渣、夹...为了提高IF钢的洁净度及可浇性,结合邯钢公司邯宝炼钢厂260 t RH真空炉生产实际,以16炉H-OLTB转炉工艺试验为样本,通过取样、夹杂物扫描电镜和数据分析等手段,对冶炼IF钢时自由脱碳模式和强制脱碳模式的终点碳含量、脱碳时间、顶渣、夹杂物数量和尺寸进行了研究,以RH精炼结束时顶渣T.Fe和T[O]的质量分数以及夹杂物数量和尺寸为依据,统计分析不同脱碳模式对顶渣氧化性和钢水洁净度的影响。研究表明,自由脱碳模式在脱碳时间和终点碳含量方面优于强制脱碳模式,自由脱碳模式平均缩短脱碳时间约3.15 min;强制脱碳模式顶渣T.Fe和FeO质量分数均低于自由脱碳模式,强制脱碳模式w[T.Fe]、w[FeO]分别为5.68%、5.45%,自由脱碳模式w[T.Fe]、w[FeO]分别为7.26%、6.84%;强制脱碳模式下的IF钢水夹杂物数量更少,自由脱碳模式和强制脱碳模式单位面积内尺寸在15μm以下的夹杂物数量分别为16.2~23.4个和10.6~14.4个。综合来看,建议H-OLTB转炉将RH炉进站初始氧含量控制在(286~408)×10^(-6),RH精炼炉冶炼IF钢采用强制脱碳模式,钢水洁净度更高。展开更多
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.
文摘为了提高IF钢的洁净度及可浇性,结合邯钢公司邯宝炼钢厂260 t RH真空炉生产实际,以16炉H-OLTB转炉工艺试验为样本,通过取样、夹杂物扫描电镜和数据分析等手段,对冶炼IF钢时自由脱碳模式和强制脱碳模式的终点碳含量、脱碳时间、顶渣、夹杂物数量和尺寸进行了研究,以RH精炼结束时顶渣T.Fe和T[O]的质量分数以及夹杂物数量和尺寸为依据,统计分析不同脱碳模式对顶渣氧化性和钢水洁净度的影响。研究表明,自由脱碳模式在脱碳时间和终点碳含量方面优于强制脱碳模式,自由脱碳模式平均缩短脱碳时间约3.15 min;强制脱碳模式顶渣T.Fe和FeO质量分数均低于自由脱碳模式,强制脱碳模式w[T.Fe]、w[FeO]分别为5.68%、5.45%,自由脱碳模式w[T.Fe]、w[FeO]分别为7.26%、6.84%;强制脱碳模式下的IF钢水夹杂物数量更少,自由脱碳模式和强制脱碳模式单位面积内尺寸在15μm以下的夹杂物数量分别为16.2~23.4个和10.6~14.4个。综合来看,建议H-OLTB转炉将RH炉进站初始氧含量控制在(286~408)×10^(-6),RH精炼炉冶炼IF钢采用强制脱碳模式,钢水洁净度更高。
