抑制气体混合物的吸收光谱中的交叉干扰
Li Wang1, Tingting Zhang1, Qinduan Zhang1
1Laser Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; Key Laboratory of Optical Fibre Sensing Technologies of Shandong Province, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
一个改进的火虫算法增强了支持矢量机器回归,用于准确的混合气体分析. 这种方法可以最大限度地减少预测甲 (CH4) 和一氧化碳 (CO) 度的错误,从而提高气体检测的准确性.
科学领域:
- 频谱学是一种光谱学.
- 化学分析 化学分析
- 机器学习 机器学习
背景情况:
- 使用可调节二极管激光吸收光谱学对混合气体进行定量分析,由于光谱重叠和干扰,面临着挑战.
- 这些问题导致了大量的测量错误和多元组件气体分析的精度降低.
研究的目的:
- 为支持矢量机回归开发一个改进的火虫算法,以提高混合气体定量分析的准确性.
- 为了解决可调节二极管激光吸收光谱中的光谱重叠和相互干扰的局限性.
主要方法:
- 开发了一种改进的火虫算法,包括适应式步骤大小,用于平衡的本地和全球搜索.
- 整合了梯度下降方法以加速参数优化,增强模型概括性和预测准确性.
- 该算法应用于支持矢量机器回归模型,用于预测气体度.
主要成果:
- 改进的算法实现了CH4的最大预测误差不超过0.0443%,CO的最大预测误差为2ppm.
- 确定系数 (R2) 达到CH4的0.9994和CO的0.99815,表明预测的准确性很高.
- 实验结果表明,拟议方法在减少由光谱干扰引起的误差方面具有有效性.
结论:
- 改进的火虫算法显著提高了使用可调节二极管激光吸收光谱学的多元组件气体分析的准确性.
- 该方法提供了一种可行且高效的方法,用于用单一光源高精度检测混合气体.
- 这项研究为现实世界的气体检测系统的进步提供了理论支持.
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