Experimental Study on the Effect of Hydrogen Addition on the Laminar Burning Velocity of Methane/Ammonia–Air Flames - Ahmed Yasiry; Jinhua Wang; Longkai Zhang; Hongchao Dai; Ahmed A. A. Abdulraheem; Haroun A. K. Shahad; Zuohua Huang (Applied Sciences, 2023)
Variations in methane–ammonia blends with hydrogen enrichment can modify premixed flame behavior and play a crucial role in achieving ultra-low carbon emissions and sustainable energy consumption. Current combustion units may co-fire ammonia/methane/hydrogen, necessitating further investigation into flame characteristics to understand the behavior of multi-component fuels. This research aims to explore the potential of replacing natural gas with ammonia while making only minor adjustments to equipment and processes. The laminar burning velocity (LBV) of binary blends, such as ammonia–methane, ammonia–hydrogen, and hydrogen–methane–air mixtures, was investigated at an equivalence ratio of 0.8–1.2, within a constant volume combustion chamber at a pressure of 0.1 MPa and temperature of 298 K. Additionally, tertiary fuels were examined with varying hydrogen blending ratios ranging from 0% to 40%. The results show that the laminar burning velocity (LBV) increases as the hydrogen fraction increases for all mixtures, while methane increases the LBV during blending with ammonia. Hydrogen-ammonia blends are the most effective mixture for increasing LBV non-linearly. Enhancement parameters demonstrate the effect of ternary fuel, which behaves similarly to equivalent methane in terms of adiabatic flame temperature and LBV achieved at 40% hydrogen. Experimental data for neat and binary mixtures were validated by different kinetics models, which also showed good consistency. The ternary fuel mixtures were also validated with these models. The Li model may qualitatively predict well for ammonia-dominated fuel. The Shrestha model may overestimate results on the rich side due to the incomplete N2Hisub-mechanism, while lean and stoichiometric conditions have better predictions. The Okafor model is always overestimated.