부산대학교 도서관

An empirical model based on machine learning is developed for predicting the variation of the laminar burning velocities of H 2 /CO/CH 4 /CO 2 /N 2 /air mixtures with volumetric fractions as the independent variables at different elevated mixture temperatures and pressures. The proposed model is derived partly based on the measured burning velocities of syngas-air mixtures at elevated temperatures and pressures using diverging channel method, and partly established from the predictions using the FFCM-1 detailed kinetic model. The experiments at elevated pressures and temperature strongly agree with the predictions of the FFCM-1 kinetic model for PG1 (H 2 /CO/CO 2 /N 2 = 15/15/15/55) syngas composition. Based on the detailed analysis of the experimental results, a power-law correlation considering the α, β variations is proposed: S u = S u,o * (T u /T u,o)α0+α1 (1−Pu/Pu,o) * (P u /P u,o)β0+β1 (1−Tu/Tu,o). Machine learning model (multiple linear-regression) was trained for the variables (S u,o , α o , α 1 , β 0 , β 1) in the power-law correlation to enable the prediction of laminar burning velocity at various pressure and temperature conditions. The empirical model was developed with mole fractions of various components (H 2 /CO/CH 4 /CO 2 /N 2) in the syngas composition and equivalence ratio as independent variables. The developed model was intended for low-calorific value syngas mixtures, and it performs exceedingly well without solving detailed governing equations, detailed chemistry, and transport equations. The proposed model is accurate for a wide range of syngas-air mixtures reported in the literature. A detailed comparison showed that the empirical model accurately predicts the laminar burning velocity with error <10%, for a wide range of H 2 /CO/CH 4 /CO 2 /N 2 /Air mixtures with 0.25 < X H 2 < 0.70 , 0.25 < X CO < 0.70, 0 < X C H 4 < 0.15, 0 < X C O 2 < 0.50, 0 < X N 2 < 0.70, for equivalence ratios of ϕ = 0.5–2.5, mixture temperatures from 300 to 650 K, and pressures from 1 to 5 atm. • Burning velocities of syngas-air simultaneously at high pressure and temperature. • Machine learning based empirical model developed for burning velocity prediction. • Quick approximation method for laminar burning velocity of syngas-air mixtures. • Proposed model accurate for the compositional variability at various conditions. [ABSTRACT FROM AUTHOR]