通过数据挖掘和皮尔森相关性分析钢铁制造过程
Susana Carrasco-López1, Martín Herrera-Trejo1, Manuel Castro-Román1
1Centro de Investigación y de Estudios Avanzados, CINVESTAV Saltillo, Av. Industria Metalúrgica No. 1062, Parque Industrial Saltillo-Ramos Arizpe, Ramos Arizpe 25900, Coahuila, Mexico.
Materials (Basel, Switzerland)
|June 19, 2024
概括
机器学习确定了控制钢铁制造中的 (Ca) 和硫 (S) 含量的关键变量. 有效的去除硫和初始的钢/渣渣条件对于成功的Ca-处理Al-杀死钢的生产至关重要.
科学领域:
- 金工程 金工程 金工程
- 材料科学 材料科学 材料科学
- 过程优化 过程优化
背景情况:
- 钢铁制造业的持续改进至关重要.
- 控制 (Ca) 和硫 (S) 含量对于 Ca 处理的 Al 钢的含量修改至关重要.
研究的目的:
- 确定影响炉精炼结束时Ca和S含量的关键过程变量.
- 应用机器学习来预测成功的钢铁制造热量.
主要方法:
- 使用决策树分类器,一种机器学习技术.
- 采用皮尔森相关性来将过程变量与根节点属性联系起来.
- 分析了硫的分布系数,以区分令人满意的和不令人满意的热量.
主要成果:
- 炼油结束时的硫分布系数是热质量的主要区分因子.
- 发现硫的分布系数和钢铁和渣中工艺结束时的S含量以及Si含量之间存在很高的相关性.
- 二次相关性涉及Si含量和渣渣的基本性与S含量.
结论:
- 在炼油开始时,初始的钢铁和废渣条件至关重要.
- 在提炼过程中有效地去除硫,对于达到所需的Ca和S水平至关重要.
- 优化这些因素导致成功的Ca-处理Al-杀钢生产.
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