Desulfurization of CaO–Al_(2)O_(3) particles in molten steel was observed in situ using high-temperature confocal scanning laser microscopy.The effects of the aluminum and silicon contents of molten steel on desulfur...Desulfurization of CaO–Al_(2)O_(3) particles in molten steel was observed in situ using high-temperature confocal scanning laser microscopy.The effects of the aluminum and silicon contents of molten steel on desulfurization were analyzed.When the total aluminum content in the steel increased from 6 to 1100 ppm,the CaS content in CaO–Al_(2)O_(3) particles increased from 2.1wt%to 84.84wt%after the reaction for 90 s.Furthermore,when the silicon content in the steel increased from 0.01wt%to 2.20wt%,the CaS content in CaO–Al_(2)O_(3) particles increased from 1.53wt%to 79.01wt%after the reaction for 90 s.This indicates that the increase in the aluminum and silicon contents of the steel promoted the desulfurization of CaO–Al_(2)O_(3) particles.A kinetic model was established to predict the CaO–Al_(2)O_(3) particles composition,and the diffusion coefficient of sulfur in CaO–Al_(2)O_(3) particles was 9.375×10^(−10)m^(2)·s^(−1) at 1600℃,which provided a new method for the calculation of diffusion coefficient.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
基金supported by the National Key R&D Program of China(No.2023YFB3709900)the National Nature Science Foundation of China(No.U22A20171)+1 种基金the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202315)the High Steel Center(HSC)at North China University of Technology and University of Science and Technology Beijing,China.
文摘Desulfurization of CaO–Al_(2)O_(3) particles in molten steel was observed in situ using high-temperature confocal scanning laser microscopy.The effects of the aluminum and silicon contents of molten steel on desulfurization were analyzed.When the total aluminum content in the steel increased from 6 to 1100 ppm,the CaS content in CaO–Al_(2)O_(3) particles increased from 2.1wt%to 84.84wt%after the reaction for 90 s.Furthermore,when the silicon content in the steel increased from 0.01wt%to 2.20wt%,the CaS content in CaO–Al_(2)O_(3) particles increased from 1.53wt%to 79.01wt%after the reaction for 90 s.This indicates that the increase in the aluminum and silicon contents of the steel promoted the desulfurization of CaO–Al_(2)O_(3) particles.A kinetic model was established to predict the CaO–Al_(2)O_(3) particles composition,and the diffusion coefficient of sulfur in CaO–Al_(2)O_(3) particles was 9.375×10^(−10)m^(2)·s^(−1) at 1600℃,which provided a new method for the calculation of diffusion coefficient.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
