Abstract
The combustion of fossil fuels is primarily responsible for disrupting the carbon cycle equilibrium by releasing greenhouse gases (GHGs). Therefore, detecting GHG emissions from fossil fuels is extremely important. In this study, utilizing laser-induced breakdown spectroscopy (LIBS), a new method for real-time in-situ detection of carbon fluctuations during combustion has been developed. The combustion of fossil fuels is emulated through the controlled burning of candles within a confined area, and the elemental content of the surrounding air during this process is analyzed. Fluctuations in the intensity of CN spectral lines were tracked to reveal changes in carbon concentration. The backpropagation neural network (BPNN) is used to identify and verify local air with different carbon concentrations, and the predictions are accurate. In conclusion, the integration of BPNN and LIBS for the purpose of identifying variations in carbon content during combustion provides an effective method for environmental management.
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