Machine learning based quantification of fuel-air equivalence ratio and pressure from laser-induced plasma spectroscopy.

In this study, we demonstrate successful development of a predictive model that detects both the fuel-air equivalence ratio (ϕ) and local pressure prior to plasma formation via machine-learning from the laser-induced plasma spectra; the resulting model enables measurement of a wide range of fuel concentrations and pressures. The process of model acquisition is composed of three steps: (i) normalization of the spectra, (ii) feature extraction and selection, and (iii) training of an artificial neural network (ANN) with feature scores and the corresponding labels. In detail, the spectra were first normalized by the total emission intensity; then principal component analysis (PCA) or independent component analysis (ICA) was carried out for feature extraction and selection. Subsequently, the scores of these principal or independent components as inputs were trained for the ANN with expected ϕ and pressure values for outputs, respectively. The model acquisition was successful, and the model's predictive performance was validated by predicting the ϕ and pressure in the test dataset.