A neural network filter based high-sensitive MIR CO2 sensor

Abstract

A low-power consumption, small-size and high measurement sensitivity mid-infrared (MIR) carbon dioxide (CO2) sensor was developed based on wavelength modulation spectroscopy (WMS) technology, utilizing a mid-infrared distributed feedback interband cascade laser (DFB-ICL) operating around 2682 nm. The sensor incorporated two key components, a home-made multi-pass gas cell with base length of 10 cm achieving 1.5 m optical path and a highly integrated FPGA board, which were used to reduce the size of the sensor. A novel second harmonic-neural network filter (SH-NNF) was also proposed and implemented in this sensor. The sensor’s performance was evaluated through a 20 mins continuous experiment using calibrated gas with 500 ppm concentration. The results, before and after SH-NNF, demonstrated an improvement in sensitivity and detection precision from 1.68 ppm (1 σ) to 1.17 ppm and from 2.14 ppm to 1.45 ppm, respectively. The optimum limit of detection of 0.392 ppm with integration time of 218 s was obtained. The ppm detection and compact characteristics of the developed MIR sensor show its potential for detecting CO2 in the ambient air.