Data-Driven Prediction of Laminar Burning Velocity for Ternary Ammonia/Hydrogen/Methane/Air Premixed Flames

Zero-carbon fuels such as hydrogen and ammonia play a pivotal role in the energy transition by offering cleaner alternatives to natural gas (methane), especially in industrial combustion systems. Binary and ternary blends of these fuels offer a transitionary, low-carbon solution in the near future. Laminar burning velocity (LBV), as a fundamental combustion property, is significantly different for ammonia, hydrogen, and methane. Although the LBV of binary blends of these fuels is well-studied, ternary blends have not been extensively studied. In this study, the primary objective is to employ a simple ensemble learning method to predict the LBV of ternary ammonia/hydrogen/methane/air mixtures. The training dataset consists of experimental data sourced from a large number of publications (3846 data points), as well as synthetic data generated by 1D freely propagating premixed flame simulations in Cantera using a detailed chemical kinetic model. Three machine learning algorithms, namely artificial neural networks, gaussian process regression, and extreme gradient boosting trees are trained and optimised. Then, a simple ensemble averaging method is used to reduce overfitting and improve robustness. The ensemble model achieves a coefficient of R2 = 0.991 with an inference time that is approximately 8,000 times faster than the 1D simulation run time. The ensemble model is capable of predict ing LBVs of ammonia/hydrogen/methane/air mixtures for T = [295−756K], P = [1 − 10bar], ϕ = [0.5 − 1.8] across all possible blending ratios.