基于光谱特征提取的转换器温度和碳含量预测模型的研究
Bo Zhao1, Jinxuan Zhao1, Wei Wu2
1Metallurgical Technology Institute, Central Iron and Steel Research Institute Co. Ltd., Beijing, 100081, People's Republic of China.
Scientific reports
|September 2, 2023
概括
转换器火焰分析使用先进的算法准确预测钢温度和碳含量. 该方法识别了关键的光谱特征,实现了对关键炼参数的高预测精度.
科学领域:
- 金工程 金工程 金工程
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 转换器口腔火焰特性作为钢温度和组成的指标.
- 预测炼过程需要分析复杂的火焰光谱数据.
研究的目的:
- 根据火焰光谱数据开发转换器炼的预测模型.
- 为了确定与钢温度和碳含量相关的关键光谱特征.
主要方法:
- 从2048维波长数据中提取特征,使用BEADS算法和基因算法优化的粗略集属性减小算法.
- 使用逆向传播 (BP) 神经网络开发温度和碳含量的预测模型.
- 使用MIV算法对模型通用性的验证.
主要成果:
- 确定了八个显著的光谱指数 (f-507,f-520,f-839,f-1073,f-1371,f-1528,f-1727,f-1826),这些指数可以被认为是显著的.
- 温度预测模型在训练,测试和验证集中实现了99%的准确性,在±5K范围内达到88.7%的命中率.
- 碳含量预测模型达到99%的准确性,94.0%的命中率在±0.05%.
结论:
- 转换器火焰光谱分析,再加上先进的特征选择和BP神经网络,为预测钢温度和碳含量提供了高度准确的方法.
- 确定的光谱特征具有代表性,并使炼结果的可靠预测成为可能.
- 开发的模型显示了钢铁制造过程中实时过程控制的巨大潜力.
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