机器学习方法用于根据编制的钢废混合物预测基本氧炉中的流浪元素
Michael Schäfer1,2, Ulrike Faltings3, Björn Glaser4
1Department of Materials Science and Engineering, KTH Royal Institute of Technology, 10044, Stockholm, Sweden. mschafer@kth.se.
Scientific reports
|January 18, 2025
概括
这项研究使用机器学习来预测钢铁生产中的化学元素. XGBoost模型准确地预测了流浪者元素的含量,改善了废料混合物优化和融质量控制.
科学领域:
- 金与材料科学 金与材料科学
- 数据科学和机器学习
背景情况:
- 钢铁生产依赖于生铁和废料,但废料成分的变化使预测最终化学分析变得复杂.
- 当前的废料组合编译通常取决于经验和试验,导致潜在的低效率.
研究的目的:
- 开发一种机器学习模型,用于预测钢铁加工机中的化学元素含量.
- 为了实现优化废料混合物利用,并保持融化质量,尽管可变的废料可用性.
主要方法:
- 利用大约115,000次加热的数据集进行模型开发.
- 应用了XGBoost机器学习算法来预测流浪元素度 (Cu, Cr, Mo, P, Ni, Sn, S).
- 实现了一个在线模型,具有同步接口,用于实时模拟和优化.
主要成果:
- 成功预测了基本氧炉过程结束时的流浪元素 (铜,,,,,,,锡和硫) 的含量.
- 证明了可以使用常规收集的预先存在的数据进行预测,而不需要额外的传感器.
- 在线模型有助于模拟新的输入材料组合,以保持融质量.
结论:
- 机器学习,特别是XGBoost,为预测钢铁转换器中的化学成分提供了可行的解决方案.
- 准确的废料管理和高质量的上游数据对于有效的模型性能至关重要.
- 开发的模型支持适应性废弃物选择,以确保稳定的钢质.
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