您当前的位置：
首页 >
文章列表页 >
Concentration prediction for overlapping gas spectra of H2S and CO2 using machine learning
Modern Applied Optics | 更新时间：2026-03-12

    • Concentration prediction for overlapping gas spectra of H2S and CO2 using machine learning

    • “The research progress in the field of flue gas monitoring was introduced, and relevant experts proposed a H2S concentration prediction method based on machine learning algorithms, providing a practical and feasible technical solution for solving the problem of spectral aliasing between H2S and CO2.”
    • Optics and Precision Engineering   Vol. 34, Issue 5, Pages: 722-733(2026)

    • DOI：10.37188/OPE.20263405.0722    

      CLC： TP181;O433
    • CSTR：32169.14.OPE.20263405.0722    

    • Received：10 December 2025，

      Revised：2025-12-30，

      Published：10 March 2026

    移动端阅览

  • XU Weichen,WANG Gengqian,GUO Songjie,et al.Concentration prediction for overlapping gas spectra of H2S and CO2 using machine learning[J].Optics and Precision Engineering,2026,34(05):722-733.

  •  
  •  

0

Views

0

下载量

0

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Feature selection enhances classification accuracy of magnesium alloys in LIBS spectra
Establishment and optimization of aerial multispectral field straw mulch quantity inversion model
Research on open circuit detection method of hydrogen sulfide/methane based on 8.309 μm QCL
Simulating primary visual cortex to improve robustness of CNN neural network structures
Gaussian process-based parameter identification and model current predictive control strategy of PMSM

Related Author

LI Chuanliang
QIU Xuanbing
WANG Xinhao
FENG Lei
XU Xiangjun
GONG Yu
CHEN Mingfang
LIU Yuanyuan

Related Institution

School of Materials Science and Engineering，Taiyuan University of Science and Technology
School of Applied Science， Taiyuan University of Science and Technology， Shanxi Precision Measurement and Online Testing Equipment Engineering Research Center
School of Electronic Information and Electrical Engineering， Shanghai Jiao Tong University
Jilin Agricultural Machinery Research Institute
Changchun Agricultural Machinery Research Institute
0