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This study investigates the impact of chemical kinetic uncertainties on biogas combustion using a Uncertainty Quantification (UQ)-based methodology. The results indicate that the variation of physicochemical properties introduced by composition variability introduces smaller uncertainties in the resulting flame properties than the Arrhenius parameters involved in the kinetics used to describe the oxidation process. We demonstrate that the use of reduced mechanisms for methane-air oxidation could be a starting point to develop optimized schemes for biogas combustion. In that regard, we adopted an embedded discrepancy approach to understanding the limits of the use of a reduced mechanism for methane/air in this renewable fuel. This strategy provides a way to reduce systematically the cost of reaction kinetics in simulations, while quantifying the accuracy of predictions of important target quantities. Finally, we develop a surrogate model for biogas flame propagation using machine learning techniques to make feasible a broader UQ analysis.

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