在生产过程中关键变量的虚拟传感:数据驱动模型的比较研究
Yating Yao1, Yupeng Xing1, Ziteng Zuo1
1Department of Chemical Equipment and Control Engineering, College of New Energy, China University of Petroleum (East China), Qingdao 266580, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
一个新的虚拟传感器模型,基于移动窗口的动态变化贝叶斯主要成分分析 (MW-DVBPCA),准确地估计了生产中的关键气体度. 这种方法克服了传统传感器的局限性,改善了过程控制.
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:
- 是一种通过天然气蒸汽改造产生的关键能源载体.
- 准确的实时监测CH4,CO,CO2和H2度对于产品质量至关重要.
- 传统的测量方法通常是缓慢或昂贵的,需要先进的解决方案.
研究的目的:
- 开发一种先进的虚拟传感器,用于估计气生产中的关键气体度.
- 解决现有的虚拟传感器在捕捉过程动态和变化的局限性.
- 提高天然气蒸汽改造实时监控的准确性和效率.
主要方法:
- 基于移动窗口的动态变化贝叶斯主要成分分析 (MW-DVBPCA) 模型的开发.
- 使用有限冲动响应范式建模过程动态.
- 通过差异演化算法自动确定运输延迟.
- 使用移动窗口方法捕捉时间变化.
主要成果:
- 通过考虑动态,时间变化和运输延迟,MW-DVBPCA模型有效估计了关键气体度.
- 进行了数据驱动虚拟传感器的比较分析.
- 该模型的性能通过使用现实中的天然气蒸汽改造生产过程来验证.
结论:
- MW-DVBPCA为生产过程提供了卓越的虚拟传感解决方案.
- 这种方法通过克服传统测量挑战,提高了实时控制和产品质量.
- 该研究验证了拟议模型在复杂的工业应用中的有效性.
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