4.7 Article

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

期刊

CHEMICAL ENGINEERING JOURNAL
卷 450, 期 -, 页码 -

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cej.2022.137860

关键词

Non -thermal plasma; Dry reforming of methane; Biogas utilization; Oxygenates; Methanol synthesis

资金

  1. European Union [813393]
  2. Marie Curie Actions (MSCA) [813393] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

A promising plasma process for the single-step conversion of methane and carbon dioxide into liquid fuels and chemicals at ambient pressure and room temperature is reported. The distribution of liquid products can be tailored by tuning the reaction conditions.
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.

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