使用SIP-PCA在热电厂的NOx排放过程中的故障检测
Mifeng Ren1, Yan Liang1, Junghui Chen2
1College of Electrical Power and Engineering, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan, 030024, China.
ISA transactions
|June 30, 2023
概括
一种新的数据驱动模型,即基于生存信息潜力的主要组件分析 (SIP-PCA),有效监测燃煤炉的NOx排放. 这种先进的方法可以改善复杂的非高斯过程的故障检测,确保符合排放标准.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 大数据需要先进的数据驱动模型来实时支持污染排放管理的决策.
- 传统的主成分分析 (PCA) 在捕捉工业过程中的复杂相互作用和非高斯分布方面存在局限性.
- 监测燃煤炉的氧化 (NOx) 排放对于遵守环境要求至关重要.
研究的目的:
- 评估一种新的数据驱动模型的可用性,用于监测燃煤炉的NOx排放.
- 解决传统PCA在处理复杂,非高斯过程变量方面的局限性.
- 开发一个有效的系统,用于早期检测故障,防止超过排放标准.
主要方法:
- 开发一种基于生存信息潜力的新型主要成分分析 (SIP-PCA) 模型.
- 利用SIP性能指数来提高PCA对非高斯数据的信息提取能力.
- 使用核密度估计来确定故障检测中的控制极限.
- 拟议的算法应用于现实世界NOx排放过程.
主要成果:
- 与传统PCA相比,SIP-PCA模型成功地从非高斯过程变量中提取了更多的信息.
- 开发的故障检测系统有效地识别了NOx排放过程中的潜在故障.
- 该算法在真实NOx排放监测场景中证明了它的能力.
结论:
- 拟议的SIP-PCA模型是复杂工业过程中监测NOx排放的可行和有效工具.
- 可以实现过程故障的早期检测,使得及时干预成为可能.
- 该方法有助于防止NOx排放超过监管标准,从而有助于更好的环境管理.
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