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通过直接的数据驱动合成,为复杂的工业系统提供动态优化器
Khalid Alhazmi1, S Mani Sarathy2
1Clean Energy Research Platform, Physical Sciences and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. khalid.alhazmi@kaust.edu.sa.
Communications engineering
|February 18, 2025
概括
本研究介绍了用于化学工艺工业 (CPI) 的数据驱动动态实时优化 (D-RTO) 方法. 该方法使用历史数据来提高运营效率,稳定性和产品质量,挑战传统方法.
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
- 数据科学数据科学数据科学
背景情况:
- 化学工艺工业 (CPI) 在平衡效率,可持续性,成本和复杂性方面面临着挑战.
- 传统的优化方法经常与动态实时调整和不确定性管理扎.
研究的目的:
- 为CPI开发一个数据驱动的动态实时优化 (D-RTO) 方法.
- 通过利用历史工厂数据来提高工艺效率,稳定性和产品质量.
主要方法:
- 构建一个值函数来评估轨迹质量.
- 采用加权回归来直接从历史数据中获得改进的优化策略.
- 将D-RTO方法应用于整个工厂的工业过程控制问题.
主要成果:
- 拟议的优化器表现出对干扰的卓越适应性.
- 该方法有效地保持了工艺稳定性和产品质量.
- 在一个现实的工业基准问题上观察到显著的效率提高.
结论:
- 数据驱动的D-RTO提供了一个实用和有效的解决方案,以提高CPI的运营效率.
- 该方法利用易于获得的历史数据,减少了对广泛建模的需求.
- 这项研究为在现实世界的工业应用中采用数据驱动优化提供了一个有希望的途径.
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