基于GA-HIDMS-PSO-BPNN模型的双气体传感吸收光谱的交叉干扰抑制
Tingting Zhang1, Chunsheng Li1, Yiwen Feng2
1Shandong Key Laboratory of Optoelectronic Sensing Technologies/National-Local Joint Engineering Laboratory for Energy and Environment Fiber Smart Sensing Technologies, Laser Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
Analytical chemistry
|August 21, 2025
概括
这项研究引入了一种新的GA-HIDMS-PSO-BPNN模型,以克服双气传感器的交叉干扰. 该模型提高了精确的甲 (CH4) 和一氧化碳 (CO) 检测的准确性和可靠性.
科学领域:
- 分析化学
- 光谱学
- 计算化学
背景情况:
- 交叉干扰从重叠的光谱限制了传统的吸收光谱的精度.
- 在各种工业应用中,精确检测甲 (CH4) 和一氧化碳 (CO) 等气体至关重要.
- 现有的方法在多元组件气体分析中难以保持稳定性和准确性.
研究的目的:
- 开发一种用于缓解可调节二极管激光吸收光谱 (TDLAS) 的新型混合模型.
- 提高双气体传感器的精度和稳定性,同时检测CH4和CO.
- 为准确的多元组件气体度测量提供强大的解决方案.
主要方法:
- 整合基因算法 (GA) 进行全球搜索.
- 应用异质改进的动态多群粒子群优化 (HIDMS-PSO) 进行局部优化.
- 使用反向传播神经网络 (BPNN) 进行非线性建模.
- 开发GA-HIDMS-PSO-BPNN混合模型
主要成果:
- 该GA-HIDMS-PSO-BPNN模型有效地解决了双气体传感器中的交叉干扰问题.
- 在1500秒的稳定性测试中,CH4的标准偏差为5000ppm,CO的标准偏差为0.3075ppm.
- 在测量CH4和CO度方面显著提高了准确性和可靠性.
结论:
- 拟议的GA-HIDMS-PSO-BPNN模型提供了一个可靠的方法,用于在交叉干扰下高精度的气体度测量.
- 这种混合型号为多元组件气体分析提供了有效的解决方案,特别是CH4和CO.
- 该研究验证了该模型在增强基于TDLAS的双气体传感器性能方面的有效性.
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