ABSTRACT This paper introduced a machine learning-based method for reconstruction spectral information from RGB images for measurements of the equivalence ratio (Φ) of premixed air-methane flames. Digital color cameras capture color and spatial information of hydrocarbon premixed flames in the visible band. The color of the flame is the result of spectral integration of chemiluminescence in the visible wavelength band, and direct use for flame equivalence ratio measurements would result in large errors due to low spectral resolution. The mapping function was used to reconstruct the spectral characteristics of the premixed methane flame for the RGB image, which were built by three types of machine learning methods: support vector regression (SVR), random forest (RF) regression, and Levenberg-Marquardt backpropagation neural network (LM-BPNN), respectively. Finally, LM-BPNN-based reconstructed spectral features are used for the development of Φ measurement model, which measurement error below 0.01, accurately measures the equivalence ratio of premixed methane flames within the range of 0.73 to 1.47. Further analyze the two-dimensional spatial distribution of flame equivalence ratio.
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