基于神经网络的别名分离光谱解算法,用于精确的中红外多元组件气体传感
Hao Xiong1,2, Ligang Shao2, Yuan Cao2
1College of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, Anhui 230026, China.
ACS sensors
|August 16, 2024
概括
一个新的中红外传感器使用神经网络算法同时检测甲,水蒸气和乙. 这种低成本,低复杂度的系统实现了对微量气体检测的高灵敏度.
科学领域:
- 频谱学是一种光谱学.
- 气体传感器可以检测到气体.
- 人工智能的人工智能是人工智能.
背景情况:
- 由于重叠的光谱线,同时检测多元组件气体具有挑战性.
- 传统方法需要具有压力传感器或多个激光器的复杂系统,增加成本和复杂性.
研究的目的:
- 开发一个低成本,低复杂度的传感器,同时检测甲 (CH4),水蒸气 (H2O) 和乙 (C2H6).
- 用激光吸收光谱学处理多元组件气体分析中的光谱干扰.
主要方法:
- 一个中红外传感器是利用波长调制光谱学开发的.
- 使用基于神经网络的算法来解别名的光谱数据.
- 该系统在实验室环境中使用艾伦偏差分析进行了评估.
主要成果:
- 传感器成功实现了同时检测CH4,H2O和C2H6.6的同时检测.
- 达到的最低检测极限为CH4的6.04ppb,H2O的118.44ppb,C2H6的1ppb,平均时间为3秒.
- 神经网络算法有效地解决了光谱干扰.
结论:
- 拟议的传感器整合了神经网络和波长调制光谱,提供了高灵敏度,低成本和降低了复杂性.
- 这项技术显示出在不同应用中同时检测多个微量气体的巨大潜力.
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