Automated Kinetic Mechanism Evaluation for e-Fuels Using SciExpeM: The Case of Oxymethylene Ethers

<div class="section abstract"><div class="htmlview paragraph">In the rapidly changing scenario of the energy transition, data-driven tools for kinetic mechanism development and testing can greatly support the evaluation of the combustion properties of new potential e-fuels. Despite the effectiveness of kinetic mechanism generation and optimization procedures and the increased availability of experimental data, integrated methodologies combining data analysis, kinetic simulations, chemical lumping, and kinetic mechanism optimization are still lacking. This paper presents an integrated workflow that combines recently developed automated tools for kinetic mechanism development and testing, from data collection to kinetic model reduction and optimization. The proposed methodology is applied to build a consistent, efficient, and well-performing kinetic mechanism for the combustion of oxymethylene ethers (OMEs), which are promising synthetic e-fuels for transportation. In fact, OMEs are easily mixed with conventional fuels and share similar ignition propensity, and are therefore potential drop-in fuels. Additionally, their oxygenated nature significantly reduces soot emissions. The proposed workflow extends our recently developed kinetic mechanism for OME₁ (dimethoxymethane – DMM) to OME_2-4: the model is derived from state-of-the-art detailed literature mechanisms, updated according to a reaction class-based approach, and simplified according to chemical lumping. Then, the model is reduced to two different skeletal versions using DRGEP method. An extensive database of ~80 datasets for kinetic mechanism testing is collected, covering different reactor types and experimental conditions. The selected datasets are uploaded to SciExpeM, a recently developed data ecosystem that allows automated kinetic mechanism performance evaluation through a multi-index approach. The performance obtained from SciExpeM shows that the lumped mechanism reproduces well the selected experimental data, and both skeletal mechanisms, well-suited to CFD and engine simulations, show equally good performance. Some minor model deficiencies identified for OME₂ and OME₃ are finally recovered via data-driven kinetic modeling optimization, which relies on the same multi-index approach adopted in SciExpeM for the kinetic model evaluation.</div></div>