通过机器学习预测高炉中的含量:一个全面的处理和建模管道
Omer Raza1, Nicholas Walla1, Tyamo Okosun1
1Center for Innovation through Visualization and Simulation (CIVS) and Steel Manufacturing Simulation and Visualization Consortium (SMSVC), Purdue University Northwest, Hammond, IN 46323, USA.
Materials (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种新的两阶段机器学习方法,用于准确预测高炉 (BF) 中的含量. 该方法使用历史操作数据实现了91%的准确性,提高了钢铁制造效率和安全性.
科学领域:
- 金工程 金工程 金工程
- 数据科学与机器学习
背景情况:
- 含量对于高炉 (BF) 的运行效率和集成钢铁制造至关重要.
- 现有的含量预测方法 (采样,第一原则和当前基于数据的模型) 在灵活性和参数选择方面存在局限性.
- 不同的BF操作参数影响热金属含量,使准确的预测变得复杂.
研究的目的:
- 开发一种综合方法,用于整合和选择相关参数,以准确预测BF中的含量.
- 为了提高安全性和降低经济风险,使条件能够动态调整.
- 建立一个强大的机器学习 (ML) 模型来预测热金属含量.
主要方法:
- 一种两阶段的方法,涉及对各种炉子参数的概括数据处理方案.
- 使用机器学习 (ML) 实现一个强大的建模管道.
- 利用XGBoost模型根据过去班的操作条件来预测含量.
主要成果:
- 在预测即将到来的炉的平均 (Si) 含量时,达到91%的准确性.
- 成功预测Si含量,仅使用过去轮班的操作条件,这些条件可以实时获得.
- 证明了基于ML的含量预测的强有力的基线,并有可能在未来进行改进.
结论:
- 拟议的两阶段ML方法提供了一种灵活而准确的方法来预测热金属含量.
- 该模型依赖于易于获取的历史数据,使其适用于实时应用.
- 这种预测能力可以显著改善高炉运行,安全和经济结果.
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