在使用RSM,MLP和RBF模型的直接还原铁工艺中优化电能消耗.
Erfan Gholamzadeh1, Ahad Ghaemi2
1School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran , Iran.
Scientific reports
|October 7, 2025
概括
通过像MLP这样的机器学习模型优化直接降解铁 (DRI) 单元,可以显著减少能源使用. 这项研究确定了关键的运营调整,以大幅节省能源和提高钢铁生产效率.
科学领域:
- 金工程 金工程 金工程
- 数据科学数据科学数据科学
- 能源管理 能源管理
背景情况:
- 直接减少铁 (DRI) 装置在钢铁制造中至关重要,但面临着重大的能源消耗挑战.
- 在DRI过程中优化能源效率对于经济可行性和环境可持续性至关重要.
研究的目的:
- 确定影响DRI单位能源消耗的关键因素.
- 开发和比较先进的机器学习模型,用于预测和优化能源使用.
- 确定最佳的操作参数,以尽量减少DRI过程中的能源消耗.
主要方法:
- 从DRI单位收集和分析运营数据.
- 采用响应表面方法 (RSM),多层感知器 (MLP) 和辐射基函数 (RBF) 神经网络进行建模.
- 使用确定系数 (R2) 和平均平方误差 (MSE) 评估模型性能.
主要成果:
- 在准确性方面,ANN模型,特别是MLP (R2=0.99601) 的表现优于RSM (R2=0.9879).
- 优化的MLP模型确定了关键参数,如冷却气体流量和主要燃烧器流量,以减少能源.
- 预计每天节省6万千瓦时,每年节省约21,900,000千瓦时 (10.34%的效率提高).
结论:
- 机器学习,特别是MLP,为优化DRI单元的能源消耗提供了一个强大的工具.
- 数据驱动的能源管理策略可以在能源密集型行业带来显著的成本节约和提高可持续性.
- 对运行参数的战略调整有效地实现了钢铁生产的能源效率大幅提高.
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