In situ diagnostic investigation on the structure distribution of inverse diffusion flames based on laser-induced breakdown spectroscopy

Wu, Runmin; Wei, Juntao; Zhang, Ben; Li, Jinyun; Song, Xudong; Su, Weiguang; Xie, Fuwei; Lv, Peng; Yu, Guangsuo

doi:10.1016/j.fuel.2021.122540

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…Over the past few decades, optical measurement methods, such as laser-induced breakdown spectroscopy (LIBS) [13,[15][16][17], tunable diode laser absorption spectroscopy (TDLAS) [18][19][20][21], intensified charge-coupled devices (ICCD) [4,22,23], spectrometry [24][25][26], and other methods [27][28][29] have been employed for flame monitoring. Specifically, as the indirect sensing methods can effectively characterize the combustion reaction through the flame, various studies have been conducted with different flame types and equivalence ratios recently, as shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, as the indirect sensing methods can effectively characterize the combustion reaction through the flame, various studies have been conducted with different flame types and equivalence ratios recently, as shown in Table 1. For example, Wu et al [17] applied LIBS to inverse diffusion methane-oxygen flame measurement to establish the correlation between the overall equivalence ratio and LIBS emission spectra intensity. In addition, Liu et al [21] used the TDLAS method for a scramjet model engine generating strong turbulent flow fields of the flame region with different equivalence ratios and flame temperatures.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Equivalence Ratio in Combustion Flame Using Chemiluminescence Emission and Deep Neural Network

Shin,

Kwon,

Kim

et al. 2023

International Journal of Energy Research

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A reliable combustion monitoring system is essential to satisfy global carbon neutrality trends. As the concentrations of emissions and flame stability are associated with the air–fuel ratio, the equivalence ratio should be continuously evaluated. In this study, a deep neural network- (DNN-) based regression model is proposed to predict the equivalence ratio of turbulent diffusion flames. Chemiluminescence signals from the OH ∗ , CH ∗ , and C2 ∗ radicals were acquired as input features. In addition, three different optical sensing views were applied to consider the future general measurement conditions. Furthermore, a loss function comparison for model training and hyperparameter tuning techniques, such as random search and Bayesian optimization, were used to improve the prediction performance. Consequently, the enhanced DNN model showed reductions in the mean absolute error and root mean square error of ~17.84% and ~12.06%, respectively, compared with the initial model. In addition, a mean absolute percentage error and R -squared value of ~3.61% and ~0.9311, respectively, were obtained. Thus, a novel sensing method has been proposed for flame monitoring systems to realize future digital transformations in the combustion industry.

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