首页> 外文期刊>International journal of hydrogen energy >Machine learning model to predict the laminar burning velocities of H_2/CO/CH_4/CO_2/N_2/air mixtures at high pressure and temperature conditions
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Machine learning model to predict the laminar burning velocities of H_2/CO/CH_4/CO_2/N_2/air mixtures at high pressure and temperature conditions

机译:机器学习模型预测高压和高温条件下H_2 / CO / CH_4 / CO_2 / N_2 /空气混合物的层流燃烧速度

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An empirical model based on machine learning is developed for predicting the variation of the laminar burning velocities of H-2/CO/CH4/CO2/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/CO2/N-2 = 15/15/15/55) syngas composition. Based on the detailed analysis of the experimental results, a power-law correlation considering the alpha, beta variations is proposed: S-u = S-u,S-o *(T-u/T-u,T-o)(alpha 0+alpha 1(1-Pu/Pu,o)) * (P-u/P-u,P-o)(beta 0+beta 1 (1-Tu/Tu,o)).Machine learning model (multiple linear-regression) was trained for the variables (S-u,S-o, alpha(0), alpha(1), beta(0), beta(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/CH4/CO2/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/CH4/CO2/N-2/Air mixtures with 0.25 < X-H2 < 0.70, 0.25 < X-CO < 0.70, 0 < X-CH4 < 0.15, 0 < X-CO2 < 0.50, 0 < X-N2 < 0.70, for equivalence ratios of phi = 0.5-2.5, mixture temperatures from 300 to 650 K, and pressures from 1 to 5 atm. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
机译:建立了基于机器学习的经验模型,以预测H-2 / CO / CH4 / CO2 / N-2 /空气混合物的层流燃烧速度的变化,其中体积分数作为自变量在不同的升高的混合物温度和压力下。提出的模型部分基于使用歧管法在高温和高压下测量的合成气-空气混合物的燃烧速度,部分基于使用FFCM-1详细动力学模型的预测而建立。在高压和高温下进行的实验与PG1(H-2 / CO / CO2 / N-2 = 15/15/15/55)合成气组成的FFCM-1动力学模型的预测非常吻合。在对实验结果进行详细分析的基础上,提出了考虑α,β变化的幂律相关性:Su = Su,So *(Tu / Tu,To)(alpha 0 + alpha 1(1-Pu / Pu, o))*(Pu / Pu,Po)(beta 0 + beta 1(1-Tu / Tu,o))。对机器学习模型(多元线性回归)进行了变量(Su,So,alpha(0 ),幂律相关性中的alpha(1),beta(0),beta(1)),以便能够预测在各种压力和温度条件下的层流燃烧速度。建立了经验模型,将合成气成分中的各种成分(H-2 / CO / CH4 / CO2 / N-2)的摩尔分数和当量比作为自变量。所开发的模型旨在用于低热值合成气混合物,并且在不解决详细的控制方程式,详细的化学反应和运输方程式的情况下,其表现极为出色。所提出的模型对于文献中报道的多种合成气-空气混合物都是准确的。详细的比较表明,对于0.25

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