Chem. J. Chinese Universities ›› 2024, Vol. 45 ›› Issue (10): 20240296.doi: 10.7503/cjcu20240296

• Physical Chemistry • Previous Articles     Next Articles

Optimization of Kinetic Mechanism for Methane Combustion Based on Machine Learning

CAO Shuangshuang1, HUANG Jiyong1, LI Wei2, ZHANG Houjun1, LI Xiangyuan3, HAN You1()   

  1. 1.School of Chemical Engineering and Technology,Tianjin University,Tianjin 300072,China
    2.National Key Laboratory of Aerospace Chemical Power,Hubei Institute of Aerospace Chemotechnology,Xiangyang 441003,China
    3.School of Chemical Engineering,Sichuan University,Chengdu 610065,China
  • Received:2024-06-19 Online:2024-10-10 Published:2024-08-12
  • Contact: HAN You E-mail:yhan@tju.edu.cn
  • Supported by:
    the Open Research Found Program of National Key Laboratory of Aerospace Chemical Power, China(STACPL220221B03);the National Natural Science Foundation of China(T2441001)

Abstract:

In this work, the experimental data of ignition delay time(T=1084—2175 K, p=7.3×104—2.4×106 Pa, φ=0.2—2.0) and laminar flame speed(T=293—600 K, p=5.1×104—1.1×106 Pa, φ=0.4—2.0) were taken as the optimization objectives based on the machine-learning model constructed by radial basis function interpolation method, and pre-exponential factors and activation energies of CH4 combustion mechanism were optimized, and a CH4 combustion mechanism that can be used in a wide range of working conditions was obtained. Compared with the Ori-CH4 mechanism, the mean error of the Opt-CH4 mechanism is reduced by 57.46% in the ignition delay times and 21.55% in the laminar flame speeds. The Opt-CH4 mechanism was used to predict the ignition delay times, laminar flame speeds and the variation tendency of species concentration in jet stirred reactor. The Opt-CH4 mechanism showed superior prediction accuracy. Under the conditions of T=1491.5 K, p=1.0×105 Pa, 4.988%CH4\19.953%O2\75.059%N2(volume fraction), the difference of sensitivity of CH3+O2CH2O+OH and CH2O+O2HCO+HO2 in each mechanism is the main reason for the difference of prediction accuracy of CH4 mechanism before and after optimization. Therefore, the machine learning method has a broad application prospect in the optimization of fuel combustion reaction kinetics mechanism parameters.

Key words: Methane combustion, Machine learning, Chemical kinetics, Mechanism optimization

CLC Number: 

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