Factor analysis and process optimization of stainless steel desulfurization process

REN Ying; ZHANG Lifeng

doi:10.13228/j.boyuan.issn0449-749x.20230401

PDF(2148 KB)

Iron and Steel ›› 2023, Vol. 58 ›› Issue (11) : 84-89. DOI: 10.13228/j.boyuan.issn0449-749x.20230401

Metallurgical Process Engineering

Factor analysis and process optimization of stainless steel desulfurization process

Author information +

History +

Abstract

In the current study,the whole process of 304 stainless steel production was investigated through industrial trial,and the change of sulfur content in steel at the AOD start,decarburization,AOD end,and LF end was analyzed and studied. The correlation between different processes and final sulfur content was determined through machine learning,and it was found that LF refining process had a greater impact on desulfurization. The LF refining was determined as the key optimization process for desulfurization of 304 stainless steels. Thermodynamic calculations were conducted to predict the reaction between the refining slag and the molten steel using the FactSage Macro Processing. With the increase of slag basicity during the LF refining,the sulfur distribution ratio of slag and steel in the LF refining increased obviously,and the sulfur content in steel decreased. Meanwhile,with the mass percent of Al₂O₃ in slag increasing from 10% to 30%,the sulfur distribution ratio between slag and steel decreased,and the sulfur content in steel increased.Increasing the basicity of the refining slag and decreasing the Al₂O₃ content were beneficial to lower the sulfur content in 304 stainless steel and improve the sulfur distribution ratio between slag and steel. A kinetic model was established for the reaction between the refining slag and the molten steel during the LF refining process. The slag-steel reaction model in the LF refining process included three reaction steps: the first step was mass transfer from the bulk steel to steel boundary layer,the second step was mass transfer from the bulk slag to slag boundary layer,and the third step was the interfacial equilibrium chemical reaction between slag boundary layer and steel boundary layer. The increase of the slag basicity was beneficial to reduce the sulfur content in steel and improve the desulfurization rate. The sulfur content after reaction rose up with the increase of initial sulfur content. A higher flow rate of argon blowing in the ladle enhanced the reaction rate,while it has little effect on the sulfur content in steel. With the increase of the initial sulfur content in steel from 0.006% to 0.012%,the sulfur content in steel also increased after the reaction,and the initial sulfur content in steel had little effect on the desulfurization rate.

Key words

machine learning / desulfurization / thermodynamic / kinetic / stainless steel

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

REN Ying, ZHANG Lifeng. Factor analysis and process optimization of stainless steel desulfurization process[J]. Iron and Steel, 2023, 58(11): 84-89 https://doi.org/10.13228/j.boyuan.issn0449-749x.20230401

References

[1] 鲁金龙,成国光,丘文生,等. 中碳高硫易切削钢中Zr添加对MnS夹杂物形貌特征的影响[J]. 钢铁研究学报,2022,34(9):963.(LU J L,CHENG G G,QIU W S,et al. Effect of Zr addition on morphology characteristics of MnS inclusions in medium carbon high sulfur free-cutting steel[J]. Journal of Iron and Steel Research,2022,34(9):963.)
[2] 张浩,赵梦豪,刘贝贝,等. 1215MS高硫钢连铸坯中硫化物三维形貌的解析[J]. 钢铁研究学报,2022,34(6):555.(ZHANG H,ZHAO M H,LIU B B,et al. Three-dimensional morphology analysis of sulfide in 1215MS high sulfur steel billet[J]. Journal of Iron and Steel Research,2022,34(6):555.)
[3] JEONG T S,CHO J H,HEO J H,et al. Desulfurization behavior of Si-killed 316L stainless steel melt by CaO-SiO₂-CaF₂-Al₂O₃-MgO slag[J]. Journal of Materials Research and Technology,2022,18:2250.
[4] 郭帅,朱航宇,韩赟,等. 夹杂物对钢塑性和韧性的影响研究进展 [J]. 钢铁研究学报,2022,34(8):713. (GUO S,ZHU H Y,HAN Y,et al. Research progress on influence of inclusion on ductility and toughness of steel[J]. Journal of Iron and Steel Research,2022,34(8):713.)
[5] 任磊,张海南,曹建强,等. Ce和退火对U75V钢中MnS夹杂物的影响[J]. 钢铁研究学报,2021,33(8):839.(REN L,ZHANG H N,CAO J Q,et al. Effect of Ce and annealing on MnS inclusions in U75V steel[J]. Journal of Iron and Steel Research,2021,33(8):839.)
[6] YOON B H,HEO K H,KIM J S,et al. Improvement of steel cleanliness by controlling slag composition[J]. Ironmaking and Steelmaking,2002,29(3):214.
[7] YAN P,GUO X,HUANG S,et al. Desulphurisation of stainless steel by using CaO-Al₂O₃ based slags during secondary metallurgy[J]. ISIJ International,2013,53(3):459.
[8] 王晓峰,栗红,曹东,等. 基于微细异相钢液洁净化技术研究进展[J]. 工程科学学报,2022,44(9):1538.(WANG X F,LI H,CAO D,et al. Progress in cleaning and purification of liquid steel technology based on fine heterogeneous phases[J]. Chinese Journal of Engineering,2022,44(9):1538.)
[9] BI Q F,GOODMAN K E,KAMINSKY J,et al. What is machine learning? A primer for the epidemiologist[J]. American Journal of Epidemiology,2019,188(12):2222.
[10] GENG X X,WANG H,XUE W H,et al. Modeling of CCT diagrams for tool steels using different machine learning techniques[J]. Computational Materials Science,2020,171:8.
[11] LEE J Y,KIM M,LEE Y K. Design of high strength medium-Mn steel using machine learning[J]. Materials Science and Engineering A:Structural Materials Properties Microstructure and Processing,2022,843:11.
[12] LI X,ZHOU X G,CAO G M,et al. Machine learning hot deformation behavior of Nb micro-alloyed steels and its extrapolation to dynamic recrystallization kinetics[J]. Metallurgical and Materials Transactions A,2021,52(7):3171.
[13] 王仲亮,顾超,王敏,等. 深度学习在炼钢过程中的研究进展及应用现状 [J]. 工程科学学报,2022,44(7):1171. (WANG Z L,GU C,WANG M,et al. Research progress and application status of deep learning in steelmaking process[J]. Chinese Journal of Engineering,2022,44(7):1171.)
[14] 杨健,吴思炜. 基于机器学习的钢铁轧制过程性能预测[J]. 钢铁,2021,56(9):1. (YANG J,WU S W. Property prediction of steel rolling process based on machine learning[J]. Iron and Steel,2021,56(9):1.)
[15] 王龙,冀秀梅,刘玠. 人工智能在钢铁工业智能制造中的应用[J]. 钢铁,2021,56(4):1.(WANG L,JI X M,LIU J. Application of artificial intelligence in intelligent manufacturing in steel industry[J]. Iron and Steel,2021,56(4):1.)
[16] 徐钢,黎敏,徐金梧. 机器学习在深冲钢质量自动判级中的应用[J]. 工程科学学报,2022,44(6):1062. (XU G,LI M,XU J W. Application of machine learning in automatic discrimination of product quality of deepdrawn steel[J]. Chinese Journal of Engineering,2022,44(6):1062.)
[17] 徐钢,黎敏,吕志民,等. 基于机器学习的产品质量在线智能监控方法[J]. 工程科学学报,2022,44(4):730. (XU G,LI M,LÜ Z M,et al. Online intelligent product quality monitoring method based on machine learning[J]. Chinese Journal of Engineering,2022,44(4):730.)
[18] SON K,LEE J G,HWANG H,et al. Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks[J]. Journal of Materials Research and Technology,2021,12:555.
[19] CARLSSON L S,SAMUELSSON P B,JONSSON P G. Interpretable machine learning-tools to interpret the predictions of a machine learning model predicting the electrical energy consumption of an electric arc furnace[J]. Steel Research International,2020,91(11):10.
[20] XIN Z,ZHANG J,LIN W,et al. Sulphide capacity prediction of CaO-SiO₂-MgO-Al₂O₃ slag system by using regularized extreme learning machine[J]. Ironmaking and Steelmaking,2021,48(3):275.
[21] 孟耀青,李建立,朱航宇,等. LF软吹时间对硅脱氧弹簧钢氧化物夹杂控制影响[J]. 钢铁,2022,57(5):48.(MENG Y Q,LI J L,ZHU H Y,et al. Effect of LF soft bubbling time on oxide inclusions in Si-killed spring steel[J]. Iron and Steel,2022,57(5):48.)
[22] 杨健,李婷婷. 稀土处理的无取向硅钢夹杂物控制研究进展 [J]. 钢铁,2022,57(7):1.(YANG J,LI T T. Research progress on inclusion control of non-oriented silicon steel with REM treatment[J]. Iron and Steel,2022,57(7):1.)
[23] CUARTAS M,RUIZ E,FERRENO D,et al. Machine learning algorithms for the prediction of non-metallic inclusions in steel wires for tire reinforcement[J]. Journal of Intelligent Manufacturing,2021,32(6):1739.
[24] ABDULSALAM M,ZHANG T S,TAN J,et al. Automated classification and analysis of non-metallic inclusion data sets[J]. Metallurgical and Materials Transactions B-Process Metallurgy and Materials Processing Science,2018,49(4):1568.
[25] BABU S R,MUSI R,THIELE K,et al. Classification of nonmetallic inclusions in steel by data-driven machine learning methods[J]. Steel Research International,2022,94:11.
[26] ABDULSALAM M,GAO N,WEBLER B A,et al. Prediction of inclusion types from bse images:RF vs. CNN[J]. Frontiers in Materials,2021,8:10.
[27] ABDULSALAM M,JACOBS M,WEBLER B A. Automated detection of non-metallic inclusion clusters in aluminum-deoxidized steel[J]. Metallurgical and Materials Transactions B,2021,52(6):3970.
[28] CHU M X,ZHAO J,LIU X P,et al. Multi-class classification for steel surface defects based on machine learning with quantile hyper-spheres[J]. Chemometrics and Intelligent Laboratory Systems,2017,168:15.
[29] LUO Q W,FANG X X,LIU L,et al. Automated visual defect detection for flat steel surface:A survey[J]. IEEE Transactions on Instrumentation and Measurement,2020,69(3):626.
[30] BOIKOV A,PAYOR V,SAVELEV R,et al. Synthetic data generation for steel defect detection and classification using deep learning[J]. Symmetry-Basel,2021,13(7):10.
[31] YU Y,ZHANG B,XUE Z. Study on desulfurization kinetics model of LF refining[J]. Advanced Materials Research,2013,690/691/692/693:246.
[32] CHEN Y,BAO Y,WANG M,et al. A mathematical model for the dynamic desulfurization process of ultra-low-sulfur steel in the LF refining process[J]. Revue de Métallurgie- International Journal of Metallurgy,2014,111(1):37.
[33] VARANASI S,PATHAK R,SAHOO K,et al. Effect of CaO-Al₂O₃-Based synthetic slag additions on desulphurisation kinetics of ladle furnace refining[J]. Transactions of the Indian Institute of Metals,2019,72(6):1.
[34] CACOULLOS T. The F-test of homoscedasticity for correlated normal variables[J]. Statistics and Probability Letters,2001,54(1):1.
[35] ZHANG Y,REN Y,ZHANG L. Kinetic study on compositional variations of inclusions,steel and slag during refining process[J]. Metallurgical Research and Technology,2018,155:415.
[36] HARADA A,MARUOKA N,SHIBATA H,et al. A kinetic model to predict the compositions of metal,slag and inclusions during ladle refining:Part 1. Basic concept and application[J]. ISIJ International,2013,53(12):2110.