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经验模式分解与深度学习相结合,用于光声谱检测混合气体度
Zhenfeng Gong1, Yeming Fan1, Yuchen Guan2
1School of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian, Liaoning 116024, China.
Analytical chemistry
|November 7, 2024
概括
这项研究引入了一种新的方法,用于准确地测量混合气体度,使用经验模式分解和深度学习. 该技术有效地解决光声谱光谱学中的光谱重叠,提高了气体分析的准确性.
科学领域:
- 频谱学是一种光谱学.
- 数据科学数据科学数据科学
- 化学分析 化学分析
背景情况:
- 精确的多元组件气体分析在各种工业和环境应用中至关重要.
- 吸收光谱重叠在光声谱学 (PAS) 挑战精确的度测量.
- 现有的方法难以从气体混合物中解复杂的光谱信号.
研究的目的:
- 开发和验证一种可靠的方法,用于从波长调制光谱学中从重叠的光谱中提取精确的气体度,并进行第二波 (WMS-2f) 检测.
- 解决使用PAS的多元组件气体分析传统方法的局限性.
- 提高混合物中乙和氨度测量的准确性和可靠性.
主要方法:
- 利用一种混合方法,将实证模态分解 (EMD) 与卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络相结合.
- 使用分布式反 (DFB) 激光在1531.5 nm处从乙-氨混合物中获取WMS-2f信号.
- 通过循环转移和高斯噪声添加来增强数据集,以提高模型的稳定性.
主要成果:
- 在模型调整后,测试组的高分类准确率达到了99.89%.
- 证明低平均绝对误差:在指定的度范围内,乙为0.092 ppm,氨为1.902 ppm.
- 从具有显著光谱重叠的复杂WMS-2f信号中成功提取了单个气体度.
结论:
- 拟议的EMD-CNN-LSTM方法为准确的多元组件气体分析提供了强大的解决方案,克服了PAS中的光谱重叠挑战.
- 这种技术显著提高了气体混合物的测量精度,特别是对乙和氨的测量精度.
- 这些发现为在各个领域更精确的气体传感应用铺平了道路.
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