Optimization of the Combustion Kinetic Mechanism and Investigation of Combustion Characteristics in NH3/CH4 Co-Firing

Existing NH3/CH4 combustion reaction mechanisms still exhibit a pronounced trade-off between predictive accuracy and computational efficiency. It is difficult to guarantee global optimality, which limits their application in gas turbine combustion simulations. The objective of this study was to introduce machine learning methods into the parameter optimization process of combustion reaction mechanisms and to construct an optimized mechanism (Bys-BP Mech) with both high accuracy and high computational efficiency. First, a reduced mechanism was obtained through mechanism coupling and the DRGEP method. Subsequently, the pre-exponential factors and activation energies of three key reactions were optimized based on sensitivity analysis. An artificial neural network was used to construct the model, and cross-validation combined with Bayesian optimization was employed to achieve automatic hyperparameter optimization. Validation results showed that the optimized Bys-BP Mech achieved an average relative error of 4.56% for LBV, and the average relative error of IDT decreased to 17.16%. CFD simulations indicated that the minimum prediction error of NO emissions was 3.74% when the CH4 co-firing ratio ranged from 40% to 70%. This mechanism addressed the limitations of existing mechanisms in combustor-scale validation under variable operating conditions and reduced the computational time of combustion simulations by half.