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

Abstract

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.

A Job Recommendation Model Based on a Two-Layer Attention Mechanism

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