High-Temperature Pyrolysis and Combustion of C5–C19 Fatty Acid Methyl Esters (FAMEs): A Lumped Kinetic Modeling Study

Abstract

In the effort to mitigate the depletion of fossil fuels and climate change, biodiesels are considered to be one of the most suitable substitutes for petro-diesel in compression ignition engine applications. As a follow up to prior modeling studies for gasoline and jet surrogate fuel components (Zhang, X.; Mani Sarathy, S. Fuel, 2021, 286, 119361), this work proposes a lumped kinetic model for both saturated and unsaturated C5–C19 fatty acid methyl esters (FAMEs) based on the same methodology. The present lumped model includes 52 FAME fuel components, covering a wide range of biodiesel surrogate fuel components, as well as components typically found in biodiesels. This methodology decouples the combustion of FAME fuels into two stages: the pyrolysis of fuel molecules and the oxidation of pyrolysis intermediates. Lumped reaction steps are used to describe the (oxidative) pyrolysis of each fuel molecule, while a detailed model (Aramcomech 2.0) is adopted as the base mechanism to describe the subsequent conversion of these key intermediates. Rate rules adopted for all the FAME fuels are consistent. The present lumped model is validated against experimental data from 20 pure FAMEs and six diesel/biodiesel surrogates, including around 130 sets of validation data. In general, the present lumped model satisfactorily captures most of these validation targets. This lumped model performs comparably with the detailed models developed in the literature under combustion conditions. Combined with the lumped model for 50 hydrocarbon fuels developed in previous work by this group, the lumped kinetic model for FAME fuels developed here can be used to predict the pyrolysis and combustion chemistry of diesel/biodiesel surrogates in CFD simulations after necessary model reduction for the base model. Also, the stoichiometric parameters of the lumped reactions for various pure FAMEs can be used as the database for data science study in FGMech development for real biodiesels.