Carrasco-López Susana, Herrera-Trejo Martín, Castro-Román Manuel, Castro-Uresti Fabián, Castro-Cedeño Edgar Iván
Centro de Investigación y de Estudios Avanzados, CINVESTAV Saltillo, Av. Industria Metalúrgica No. 1062, Parque Industrial Saltillo-Ramos Arizpe, Ramos Arizpe 25900, Coahuila, Mexico.
Ternium México, San Nicolás de los Garza 66450, Nuevo León, Mexico.
Materials (Basel). 2024 Jun 6;17(11):2786. doi: 10.3390/ma17112786.
The continuous improvement of the steelmaking process is a critical issue for steelmakers. In the production of Ca-treated Al-killed steel, the Ca and S contents are controlled for successful inclusion modification treatment. In this study, a machine learning technique was used to build a decision tree classifier and thus identify the process variables that most influence the desired Ca and S contents at the end of ladle furnace refining. The attribute of the root node of the decision tree was correlated with process variables via the Pearson formalism. Thus, the attribute of the root node corresponded to the sulfur distribution coefficient at the end of the refining process, and its value allowed for the discrimination of satisfactory heats from unsatisfactory heats. The variables with higher correlation with the sulfur distribution coefficient were the content of sulfur in both steel and slag at the end of the refining process, as well as the Si content at that stage of the process. As secondary variables, the Si content and the basicity of the slag at the end of the refining process were correlated with the S content in the steel and slag, respectively, at that stage. The analysis showed that the conditions of steel and slag at the beginning of the refining process and the efficient S removal during the refining process are crucial for reaching desired Ca and S contents.
炼钢工艺的持续改进是钢铁制造商面临的关键问题。在钙处理铝镇静钢的生产中，控制钙和硫的含量对于成功进行夹杂物改性处理至关重要。在本研究中，使用机器学习技术构建决策树分类器，从而识别在钢包精炼结束时对所需钙和硫含量影响最大的工艺变量。决策树的根节点属性通过皮尔逊形式与工艺变量相关联。因此，根节点的属性对应于精炼过程结束时的硫分配系数，其值能够区分合格炉次和不合格炉次。与硫分配系数相关性较高的变量是精炼过程结束时钢和炉渣中的硫含量，以及该阶段的硅含量。作为次要变量，精炼过程结束时的硅含量和炉渣碱度分别与该阶段钢和炉渣中的硫含量相关。分析表明，精炼过程开始时钢和炉渣的条件以及精炼过程中有效的硫去除对于达到所需的钙和硫含量至关重要。
