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超低碳钢BOF渣成分对终点[ C ]含量影响
引用本文:张明博,秦哲,仇圣桃,朱荣,白瑞国,张兴利. 超低碳钢BOF渣成分对终点[ C ]含量影响[J]. 北京科技大学学报, 2015, 0(11): 1422-1428. DOI: 10.13374/j.issn2095-9389.2015.11.005
作者姓名:张明博  秦哲  仇圣桃  朱荣  白瑞国  张兴利
作者单位:1. 北京科技大学冶金与生态工程学院, 北京100083;钢铁研究总院连铸技术国家工程研究中心, 北京100081;河北钢铁股份有限公司承德分公司, 承德067002;2. 钢铁研究总院连铸技术国家工程研究中心,北京,100081;3. 北京科技大学冶金与生态工程学院,北京,100083;4. 河北钢铁股份有限公司承德分公司,承德,067002
摘    要:采用共存理论、动力学分析和实验验证的方法,研究转炉冶炼超低碳钢吹炼末期炉渣成分对终点[ C]含量的影响规律,建立1853~1973 K时终点[ C]与炉渣成分和温度的回归模型. 结果表明:FeO活度受温度影响较小,主要受炉渣成分的影响;脱碳动力学条件主要受炉渣成分和温度的影响. 炉渣碱度增加,终点[ C]含量升高;渣中FeO含量增加,终点[ C]含量迅速降低,渣中FeO质量分数应控制在12. 0% ~18. 0%之间;渣中MgO质量分数在7. 0% ~13. 0%范围内逐渐增加,钢液中[ C]质量分数增加值不足0. 01%;随着温度的增加,钢液中[ C]含量降低. 回归模型对冶炼超低碳钢的转炉终点[ C]含量的预判平均误差率为± 15. 25%,[ C]含量误差在± 0. 01%以内的炉次占69. 19%.

关 键 词:炼钢  低碳钢  渣成分  碳含量  回归模型

Effect of BOF slag composition on the end-point [ C] content of ultra-low carbon steel
Abstract:The effect of slag composition on the [ C] content of ultra-low carbon steel at the end of smelting was investigated by the coexistence theory, dynamics analysis and experimental verification. A regression model of end-point [ C] content was established with temperature and slag composition in a temperature range from 1853 K to 1973 K. The results show that the FeO activity is little affected by temperature, while it is mainly affected by slag composition. However, the decarburization kinetics is mainly affected by slag composition and temperature. The end-point [ C] content increases with increasing slag basicity, but it quickly decreases with increasing FeO content in slag, so the end-point FeO mass fraction in slag should be controlled in 12% -18%. In addition, the incre-ment value of [ C] is less than 0. 01% with the MgO mass fraction in slag from 7% to 13%. The results also show that the end-point [ C] mass fraction decreases with increasing temperature. Compared with industrial results, the average error rate of the regression model for the end-point [ C] content in the ultra-low carbon steel is ± 15. 25%, and the hit rate to [ C] content predicted by the regression model reaches 69. 19% with a control precision of ± 0. 01% for heats of the experiment.
Keywords:steelmaking  low carbon steel  slag composition  carbon content  regression models
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