Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization

Pengbo Li; Guanyu Lin; Jianbo Chen; Jianing Wang

doi:10.3390/s24165226

Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization

Sensors (Basel). 2024 Aug 13;24(16):5226. doi: 10.3390/s24165226.

Authors

Pengbo Li^{1

2}, Guanyu Lin¹, Jianbo Chen³, Jianing Wang¹

Affiliations

¹ Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
² University of Chinese Academy of Sciences, Beijing 101408, China.
³ School of optoelectronic engineering, Changchun University of Science and Technology, Changchun 130012, China.

Abstract

Accurately detecting atmospheric carbon dioxide is a vital part of responding to the global greenhouse effect. Conventional off-axis integral cavity detection systems are computationally intensive and susceptible to environmental factors. This study deploys an Extreme Learning Machine model incorporating a cascaded integrator comb (CIC) filter into the off-axis integrating cavity. It is shown that appropriate parameters can effectively improve the performance of the instrument in terms of lower detection limit, accuracy, and root mean square deviation. The proposed method is incorporated successfully into a monitoring station situated near an industrial area for detecting atmospheric carbon dioxide (CO₂) concentration daily.

Keywords: greenhouse gas; machine learning; off-axis integrating cavity output spectrum; trace gas detection.

Grants and funding

National Natural Science Foundation of China (NO. 62005268), Key Research and Development; Program of Jilin Province (NO. 20220203195SF), Youth Innovation Promotion Association CAS; no. 2023229, and Open bidding for selecting the best candidates of Changchun City (23JG06).