  A deep learning strategy is developed for fast and accurate gas property
measurements using flame emission spectroscopy (FES). Particularly, the
short-gated fast FES is essential to resolve fast-evolving combustion
behaviors. However, as the exposure time for capturing the flame emission
spectrum gets shorter, the signal-to-noise ratio (SNR) decreases, and
characteristic spectral features indicating the gas properties become
relatively weaker. Then, the property estimation based on the short-gated
spectrum is difficult and inaccurate. Denoising convolutional neural networks
(CNN) can enhance the SNR of the short-gated spectrum. A new CNN architecture
including a reversible down- and up-sampling (DU) operator and a loss function
based on proper orthogonal decomposition (POD) coefficients is proposed. For
training and testing the CNN, flame chemiluminescence spectra were captured
from a stable methane-air flat flame using a portable spectrometer (spectral
range: 250 - 850 nm, resolution: 0.5 nm) with varied equivalence ratio (0.8 -
1.2), pressure (1 - 10 bar), and exposure time (0.05, 0.2, 0.4, and 2 s). The
long exposure (2 s) spectra were used as the ground truth when training the
denoising CNN. A kriging model with POD is trained by the long-gated spectra
for calibration, and then the prediction of the gas properties taking the
denoised short-gated spectrum as the input: The property prediction errors of
pressure and equivalence ratio were remarkably lowered in spite of the low SNR
attendant with reduced exposure.
