Dimethyl ether (DME) and dimethoxymethane (DMM) as reaction enhancers for methane: Combining flame experiments with model-assisted exploration of a polygeneration process

The potential of dimethyl ether (DME) and dimethoxymethane (DMM), representatives the attractive oxymethylene (OME) alternative fuel family, are explored here as reactivity enhancers for methane-fueled polygeneration processes. Typically, such processes that can flexibly generate power, heat, or chemicals, operate under fuel-rich conditions in gas turbines internal combustion engines. To provide a consistent basis underlying reaction mechanisms, it is recognized speciation data DME/CH4 combination available while information DMM/CH4 system largely lacking. In addition, should be noted detailed study flames, i.e., systems involving chemistry transport over large temperature range, still missing spite to extended species information. systematic approach using with electron ionization molecular-beam mass spectrometry (EI-MBMS), we thus report, first step, investigation six premixed flames DME DMM their blends methane special attention on interesting chemicals. Secondly, comprehensive but compact DME/DMM/CH4 model (PolyMech2.1) developed based these data. This then examined against experimental from various facilities, focusing preferentially elevated pressure conditions. Comparison existing literature models also included this evaluation. Thirdly, an analysis given basis, via extensively tested PolyMech2.1 model, assumed homogeneous charge compression ignition (HCCI) engine environment. main interest model-assisted exploration evaluate whether addition process lead potentially useful production syngas other along work heat. flame results show high yields, up ?78% CO ?35% H2, obtained burnt gases. From number intermediates detected, predominantly acetylene, ethylene, ethane, formaldehyde yields 2.1?4.4% (C2 hydrocarbons) 3.4?8.7% (CH2O), respectively. Also, methanol methyl formate comparably 0.6?6.7% DMM, which 1–2 orders magnitude higher than those additive. modeling-assisted process, seen perform at significantly reduced computational costs compared recently validated without sacrificing prediction performance. Promising combinations identified 77% together heat output exergetic efficiencies 89% DME.