双向长短期记忆在分离NO和SO2紫外差分吸收频谱信号中的应用
Bo Peng1, Zhen Tang1, Shiji Xian1
1Liangjiang School of Artificial Intelligence, Chongqing University of Technology, China.
概括
精确监测工业氧化 (NO) 和二氧化硫 (SO2) 排放对于环境保护至关重要. 这项研究引入了一种先进的UV-DOAS系统,使用Bi-LSTM算法进行精确的超低度检测.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 光学工程是指光学工程.
背景情况:
- 工业化石燃料的燃烧释放出有害的氧化 (NO) 和二氧化硫 (SO2).
- 精确测量这些污染物的超低度是技术上具有挑战性的.
- 现有的方法在SO2和NO之间的交叉干扰方面扎.
研究的目的:
- 开发一种新的光学测量系统,用于精确检测超低度的SO2和NO.
- 为了克服SO2和NO在混合气体样本中的相互干扰的挑战.
- 建立可靠的工业排放环境监测方法.
主要方法:
- 在200-230nm范围内利用紫外差光学吸收光谱学 (UV-DOAS).
- 开发了一种双向长短期记忆 (Bi-LSTM) 算法,以提取差异光密度并减轻干扰.
- 采用非线性校准模型来实现精确的气体度逆转.
主要成果:
- 对SO2的检测极限 (DL) 为0.27ppm,全尺度误差为3.15%.
- 对NO的DL达到0.32ppm,全尺度误差为2.81%.
- 计算的低不确定性:SO2的1.73%和NO检测的1.96%.
结论:
- 开发的UV-DOAS系统有效地测量了超低的SO2和NO度.
- Bi-LSTM算法成功地解决了SO2-NO干扰,提高了测量准确度.
- 这种方法为工业污染物的环境监测提供了一个有希望的解决方案.
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