Auto-ignition of oxymethylene ethers (OMEn, n = 2–4) as promising synthetic e-fuels from renewable electricity: shock tube experiments and automatic mechanism generation

• Published in: Fuel (Guildford) 2020-03, Vol.264, p.116711, Article 116711
• Main Authors: Cai, Liming, Jacobs, Sascha, Langer, Raymond, vom Lehn, Florian, Heufer, Karl Alexander, Pitsch, Heinz
• Format: Article
• Language: English
• Subjects: Automatic mechanism generation; Carbon dioxide; Carbon sequestration; Compression; Delay time; Electricity; Ethers; Exhaust gases; Fuels; Ignition; Ignition delay times; Initial conditions; Mechanism optimization; Model accuracy; Optimization; Organic chemistry; Oxymethylene ethers (OMEs); Spontaneous combustion; Synthetic fuels
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2019.116711

•The auto-ignition of OME2–4 investigated experimentally in a shock tube.•Automatic mechanism generator based on reaction classes and rate rules developed.•Highly automatic model development process proposed, which is composed of automatic mechanism generation and optimization.•Successful automatic mechanism generation for the auto-ignition of OME2–4. Carbon-neutral synthetic fuels can be produced from renewable electricity by the hydrogenation of carbon dioxide captured from air or exhaust gas. A promising class of these synthetic fuels are long-chain oxymethylene ethers (OMEs), which exhibit good auto-ignition characteristics for compression-ignition engine application. This study aims to investigate the auto-ignition of three oxymethylene ethers (OMEn, n = 2–4) numerically and experimentally. A shock tube is applied to measure ignition delay times over a range of initial conditions and the obtained results serve as the validation and optimization targets for a chemical mechanism of OME2-4 developed in this work. This model is derived first using an automatic reaction class-based mechanism generator. To ensure the chemical validity of the mechanism, the automatic generator applies reaction classes and rate rules consistently for OME2-4, which are adopted from a recently published OME1 mechanism. For improved model prediction accuracy of ignition delay times, the mechanism is then optimized automatically by calibrating these rate rules within their uncertainties using data for all OMEn fuels. It is shown that this highly automated model development process is able to provide accurate chemical mechanisms for large fuel components in a very efficient manner, if accurate prior kinetic knowledge exists for their short-chain counterparts.