软传感器驱动的空间时间周期协同预测控制高炉气流的高炉气流
Yaxian Zhang1, Kai Guo1, Zejun Yu1
1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
ISA transactions
|January 8, 2026
概括
这项研究引入了一种用于高炉实时气流监测的新框架,通过先进的建模和动态内存来增强预测控制. 该方法有效地解决了复杂的过程动态和时间延迟,以改善工业运营.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程控制 过程控制
- 人工智能的人工智能
背景情况:
- 高炉铁制造涉及复杂的时空动态,周期性行为和时间变化的延迟.
- 由于这些固有的过程复杂性,对气流分布的实时监控具有挑战性.
研究的目的:
- 开发高炉气流分配的协同预测控制框架.
- 通过解决建模不准确性和动态合来增强实时监控能力.
主要方法:
- 一个软传感器驱动的近接政策优化 (PPO) 框架与时空周期建模和动态内存 (SPDM-PPO) 被提出.
- 开发了一种使用空间规范化和相互信息的动态时间延迟优化方法.
- 采用了双编码的变压器网络和动态周期状态存储器 (DCSM) 机制.
- 一个合作的双优化器训练PPO策略与LSTM编码器解码器被集成为闭环控制.
主要成果:
- 拟议的SPDM-PPO框架有效地模拟了时空周期性模式和全球动态.
- DCSM机制解决了状态表示中的信息冗余和内存过时问题.
- 该框架在控制气流分配方面取得了强大而卓越的性能,并根据工业数据进行了验证.
结论:
- SPDM-PPO框架在高炉气流控制方面取得了重大进展.
- 协同方法提高了预测控制准确性和实时监控能力.
- 该方法为具有动态和周期性特征的复杂工业过程提供了强大的解决方案.
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