超过水处理工艺的长期统计过程监测
Taylor R Grimm1, Amos Branch2, Kyle A Thompson2
1Department of Statistical Science, Baylor University, Waco, Texas 76798, United States.
概括
水处理中的数据驱动故障检测通过基于PCA的方法得到了改进. 这种方法应用于超过系统的数据,显示了长期监测对传统方法的实质性优势.
科学领域:
- 环境工程 环境工程
- 水处理技术水处理技术
- 过程监控 过程监控
背景情况:
- 水处理技术的进步产生了大量的数据集,使实时,数据驱动的故障检测成为可能.
- 现有的故障检测方法通常在短期的案例研究中进行评估,这限制了对长期性能的理解.
研究的目的:
- 评估用于损害过程数据的统计和机器学习方法.
- 评估基于主要组件分析 (PCA) 的超系统故障检测方法的长期性能.
主要方法:
- 对数据损害的多种统计和机器学习方法进行了评估.
- 将基于PCA的故障检测方法应用于用于监测水质和膜污染的数据.
- 在一年的运营数据中验证了方法,测试了关键的调整参数.
主要成果:
- 根据短期病例研究,选择了自适应式激光断方法.
- 与行业标准相比,基于PCA的多变量监测方法在一年内显示出了相当大的好处.
- 仔细评估关键调参数对于成功的长期自主监控至关重要.
结论:
- 使用PCA对被扣留数据的多变量监测为水处理系统的长期性能提供了显著的优势.
- 与PCA结合的自适应式拉索检测方法为实时故障检测提供了强大的解决方案.
- 优化调整参数对于先进故障检测系统的可靠自主运行至关重要.
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