集成深度学习驱动的转换器能源系统的多阶段蒸汽预测和调度优化
Yan Hu1, Baoshan Huang1, Chao Gao2
1School of Computer Science and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
Scientific reports
|March 5, 2026
概括
本研究引入了钢铁制造业转换器蒸汽的两阶段预测框架. 它通过整合规划和生产数据来提高能源效率,以便准确地预测和安排蒸汽.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 工业工程 工业工程 工业工程
背景情况:
- 转换器蒸汽是钢铁制造业中至关重要的二次能源,对于降低成本至关重要.
- 目前用于转换器蒸汽的预测方法通常是单阶段的,并且无法有效地整合规划和生产数据.
- 蒸汽发电的时间分段性质,有限的规划数据和波动的生产信号,给预测带来了挑战.
研究的目的:
- 为转换器蒸汽开发一个综合的两阶段,多时间尺度的预测框架.
- 为了弥合长期规划预测和短期生产预测之间的差距,以改善蒸汽调度.
- 提高转换器蒸汽的回收和利用,以降低钢铁制造业的能源成本.
主要方法:
- 制定了一个分段时间的预测工作流程,将计划和生产阶段联系起来.
- 在规划阶段使用超参数优化的支持矢量机 (IBKA-SVM) 进行间隔蒸汽输出预测.
- 在生产阶段利用DSC-变压器模型进行细粒度滚动蒸汽流量预测,动态更新基线.
主要成果:
- 在规划阶段,IBKA-SVM模型实现了92.3%的预测性能.
- 在生产阶段,DSC-变压器模型实现了98.7%的预测性能.
- 拟议的框架在对工业数据的实验中始终优于传统的基线模型.
结论:
- 综合的两阶段框架为转换器蒸汽调度提供了实用,多时间尺度的预测支持.
- 这些模型通过改善蒸汽回收和利用来提高能源效率的运行.
- 这种方法解决了对时间分割的工业数据单阶段预测的局限性.
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