Catalyst-free single-step plasma reforming of CH4 and CO2 to higher value oxygenates under ambient conditions

被引:81
|
作者
Wang, Yaolin [1 ]
Chen, Yanzhen [1 ]
Harding, Jonathan [1 ]
He, Hongyuan [1 ]
Bogaerts, Annemie [2 ]
Tu, Xin [1 ]
机构
[1] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Lancashire, England
[2] Univ Antwerp, Dept Chem, Res Grp PLASMANT, Universiteitsplein 1, BE-2610 Antwerp, Belgium
关键词
Non -thermal plasma; Dry reforming of methane; Biogas utilization; Oxygenates; Methanol synthesis; ATMOSPHERIC-PRESSURE; ROOM-TEMPERATURE; METHANE; CONVERSION; REACTOR; CH4; SYNGAS; PREDICTION; GAS;
D O I
10.1016/j.cej.2022.137860
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Direct conversion of CH4 and CO2 to liquid fuels and chemicals under mild conditions is appealing for biogas conversion and utilization but challenging due to the inert nature of both gases. Herein, we report a promising plasma process for the catalyst-free single-step conversion of CH4 and CO2 into higher value oxygenates (i.e., methanol, acetic acid, ethanol, and acetone) at ambient pressure and room temperature using a water-cooled dielectric barrier discharge (DBD) reactor, with methanol being the main liquid product. The distribution of liquid products could be tailored by tuning the discharge power, reaction temperature and residence time. Lower discharge powers (10-15 W) and reaction temperatures (5-20 degrees C) were favourable for the production of liquid products, achieving the highest methanol selectivity of 43% at 5 degrees C and 15 W. A higher discharge power and reaction temperature, on the other hand, produced more gaseous products, particularly H2 (up to 26% selectivity) and CO (up to 33% selectivity). In addition, varying these process parameters (discharge power, reaction temperature and residence time) resulted in a simultaneous change in key discharge properties, such as mean electron energy (Ee), electron density (ne) and specific energy input (SEI), all of which are essential determiners of plasma chemical reactions. According to the results of artificial neural network (ANN) models, the relative importance of these process parameters and key discharge indicators on reaction performance follows the order: discharge power > reaction temperature > residence time, and SEI > ne > Ee, respectively. This work provides new insights into the contributions and tuning mechanism of multiple parameters for optimizing the reaction performance (e.g., liquid production) in the plasma gas conversion process.
引用
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页数:13
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