基于PI-LSTM的转换器重新吹风阶段的实时碳含量预测模型
Yuanzheng Guo1,2, Dongfeng He1,2, Xiaolong Li1,2
1State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了基于物理的长短期内存 (PI-LSTM) 网络,用于在钢转换器重新吹风过程中准确的实时碳含量预测. 与现有方法相比,PI-LSTM模型显著提高了预测准确性和物理可信性.
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
- 人工智能在制造业中的应用
背景情况:
- 精确预测转换器再吹中的碳含量对于钢铁生产效率和质量控制至关重要.
- 现有的模型 (机械,数据驱动,整体) 在准确性,概括性,可解释性或数据延迟方面存在局限性.
研究的目的:
- 为转换器的重新吹风阶段开发实时碳含量预测模型.
- 通过将物理原理与数据驱动方法相结合,克服当前预测方法的局限性.
主要方法:
- 提出了一个基于物理的长短期记忆 (PI-LSTM) 网络.
- 一个双分支网络处理了烟气数据,并预测了碳含量和运动参数.
- 联合损失函数和灰狼优化 (GWO) 算法用于训练和超参数调整.
主要成果:
- PI-LSTM模型表现出卓越的性能,MAE为0.0077和RMSE为0.0112.
- 实现了高终点碳含量成功率:53.71% (±0.005%),82.23% (±0.01%),和95.45% (±0.015%).这些都是成功率.
- 与机械和LSTM模型相比,预测准确度和物理可信度显著提高.
结论:
- 该PI-LSTM模型提供了一个强大的解决方案,用于实时预测转换器重新吹风中的碳含量.
- 这种方法增强了动态闭环控制,以优化钢铁生产.
- 该模型的物理约束的整合提高了可靠性和可解释性.
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