Zero-dimensional numerical simulation of dry reforming of methane in atmospheric pressure non-equilibrium plasma

Zhong Wang-Shen; Chen Ye-Li; Qian Mu-Yang; Liu San-Qiu; Zhang Jia-Liang; Wang De-Zhen

doi:10.7498/aps.70.20201700

Abstract

Recently, atmospheric non-equilibrium plasma has been proposed as a potential and novel type of “reaction carrier” for the activation and conversion of greenhouse gases (methane and carbon dioxide) into value-added chemicals, due to its unique non-equilibrium characteristics. In this paper, a zero-dimensional plasma chemical reaction kinetic model in CH₄/CO₂ gas mixture is constructed, with an emphasis on reaction mechanism for plasma dry reforming of methane to syngas and oxygenates. Especially, the effect of the CH₄ molar fraction (5%–95%) on plasma dry reforming of methane is investigated. First, the time evolution of electron temperature and density with initial methane content is presented, and the results show that both the electron temperature and electron density vary periodically with the applied triangular power density pulse, and the higher initial methane content in gas mixture is favored for a larger electron temperature and density. Subsequently, the time evolution of number densities of free radicals, ions and molecules at different CH₄/CO₂ molar fraction are given. The higher the initial methane content, the greater the number densities of H, H^–, H₂, and CH₃, leading to insufficient oxygen atoms to participate in the reaction for oxygenates synthesis. The conversions of inlet gases, the selectivities of syngas and important oxygenates are also calculated. The conversion rate of carbon dioxide increases with the increasing methane content, but the conversion rate of methane is insensitive to the variation of methane content. As methane mole fraction is increased from 5% to 95%, the selectivities of important oxygenates (CH₃OH and CH₂O) are relatively low (<5%), and the selectivity of H₂ gradually increases from 13.0% to 24.6%, while the selectivity of CO significantly decreases from 58.9% to 9.7%. Moreover, the dominant reaction pathways governing production and destruction of H₂, CO, CH₂O and CH₃OH are determined, and CH₃ and OH radicals are found to be the key intermediate for the production of valuable oxygenates. Finally, a schematic overview of the transformation relationship between dominant plasma species is summarized and shown to clearly reveal intrinsic reaction mechanism of dry reforming of methane in atmospheric non-equilibrium plasma.

Keywords:

Authors and contacts

1.
Department of Physics, Nanchang University, Nanchang 330031, China
2.
School of Physics, Dalian University of Technology, Dalian 116024, China

Corresponding author: Qian Mu-Yang, qianmuyang@ncu.edu.cn

Funds: Project supported by National Natural Science Foundation of China (Grant Nos. 12065019, 11705080)

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References

[1]	Gangadharan P, Kanchi K C, Lou H H 2012 Chem. Eng. Res. Des. 90 1956 Google Scholar
[2]	Asinger F 1986 Methanol-Chemie und Energierohstoff (Heidelberg: Springer) pp1−9
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[22]	Liu S, Winter L R, Chen J G 2020 ACS Catal. 10 2855 Google Scholar
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[32]	Bai C J, Wang L J, Li L, Dong X, Xiao Q H, Liu Z Q, Sun J H, Pan J 2019 AIP Adv. 9 035023 Google Scholar

Cited By

图 1 甲烷摩尔分数为10%, 30%, 50%, 70%和90%时的电子温度随时间演化规律

Figure 1. Electron temperature as a function of time for methane mole fractions of 10%, 30%, 50%, 70% and 90%.

DownLoad: Full-Size Img PowerPoint

图 2 甲烷摩尔分数为10%, 30%, 50%, 70%和90%时的电子密度随时间变化趋势

Figure 2. Electron density as a function of time for methane mole fractions of 10%, 30%, 50%, 70% and 90%.

DownLoad: Full-Size Img PowerPoint

图 3 (a) 10%, (b) 50%和(c) 90%甲烷摩尔分数时主要自由基的数密度随时间变化趋势, 以及(d) 主要自由基的周期平均值随甲烷摩尔分数的变化

Figure 3. The number densities of main radicals as a function of time for methane mole fractions of (a) 10%, (b) 50%, (c) 90%, and (d) time averaged number densities of main radicals as a function of initial CH₄ fraction.

DownLoad: Full-Size Img PowerPoint

图 4 (a) 10%, (b) 50%和(c) 90%甲烷摩尔分数时主要离子的数密度随时间的变化趋势, 以及(d)主要离子的周期平均值随甲烷摩尔分数的变化

Figure 4. The number densities of main ions as a function of time for methane mole fractions of (a) 10%, (b) 50%, (c) 90%, and (d) time averaged densities of main ions as a function of initial CH₄ fraction.

DownLoad: Full-Size Img PowerPoint

图 5 (a) 10%, (b) 50%和(c) 90%甲烷摩尔分数时主要分子数密度随时间的变化规律, 及(d)主要分子的周期平均值随甲烷摩尔分数的变化

Figure 5. The number densities of main molecules as a function of time for methane mole fractions of (a) 10%, (b) 50%, (c) 90%, and (d) time averaged densities of main molecules as a function of initial CH₄ fraction.

DownLoad: Full-Size Img PowerPoint

图 6 (a)进料气体的转化率和(b)合成气和重要含氧化合物的选择性随着甲烷摩尔分数的变化趋势

Figure 6. Time-averaged (a) conversion, (b) selectivity as a function of initial CH₄ mole fraction.

DownLoad: Full-Size Img PowerPoint

图 10 H₂的主要生成和损耗反应的时间平均反应速率随甲烷摩尔分数的变化柱状图　(a) 10%, (b) 50%, (c) 90%

Figure 10. Time-averaged reaction rates of the dominant reaction pathways for the production and consumption of H₂ as a function of methane mole fraction (a) 10%, (b) 50%, (c) 90%.

DownLoad: Full-Size Img PowerPoint

图 8 CH₃OH的主要生成和损耗反应的时间平均反应速率随甲烷摩尔分数的变化柱状图　(a) 10%, (b) 50%, (c) 90%

Figure 8. Time-averaged reaction rates of the dominant reaction pathways for the production and consumption of CH₃OH as a function of methane mole fraction: (a) 10%, (b) 50%, (c) 90%.

DownLoad: Full-Size Img PowerPoint

图 9 CO的主要生成和损耗反应的时间平均反应速率随甲烷摩尔分数的变化柱状图　(a) 10%, (b) 50%, (c) 90%

Figure 9. Time-averaged reaction rates of the dominant reaction pathways for the production and consumption of CO as a function of methane mole fraction (a) 10%, (b) 50%, (c) 90%.

DownLoad: Full-Size Img PowerPoint

图 7 CH₂O的主要生成和损耗反应的时间平均反应速率随甲烷摩尔分数的变化柱状图　(a) 10%, (b) 50%, (c) 90%

Figure 7. Time-averaged reaction rates of the dominant reaction pathways for the production and consumption of CH₂O as a function of methane mole fraction: (a) 10%, (b) 50%, (c) 90%.

DownLoad: Full-Size Img PowerPoint

图 11 CH₄/CO₂摩尔分数比为1:1的大气压非平衡等离子体DRM反应总体流程图. 箭头的粗细与时间平均的净反应速率成线性正比

Figure 11. Schematic overview of the dominant reaction pathways for the conversion of CH₄ and CO₂ into representative higher oxygenates and syngas in atmospheric non-equilibrium plasma for a 1:1 CH₄/CO₂ gas mixture. The thickness of the arrows is linearly proportional to time-averaged rate of net reaction.

DownLoad: Full-Size Img PowerPoint

map

[1]	Gangadharan P, Kanchi K C, Lou H H 2012 Chem. Eng. Res. Des. 90 1956 Google Scholar
[2]	Asinger F 1986 Methanol-Chemie und Energierohstoff (Heidelberg: Springer) pp1−9
[3]	Fakley M E, Jennings J R, Spencer M S 1989 J. Catal. 118 483 Google Scholar
[4]	Abdulrasheed A, Jalil A A, Gambo Y, Ibrahim M, Hambali H U, Hamid M Y S 2019 Renewable Sustainable Energy Rev. 108 175 Google Scholar
[5]	Jang W J, Shim J O, Kim H M, Yoo S Y, Roh H S 2019 Catal. Today 324 15 Google Scholar
[6]	Aramouni N A K, Touma J G, Tarboush B A, Zeaiter J, Ahmad M N 2018 Renewable Sustainable Energy Rev. 82 2570 Google Scholar
[7]	Wang Y, Yao L, Wang S, Mao D, Hu C 2018 Fuel Process. Technol. 169 199 Google Scholar
[8]	Abdullah B, Ghani N A A, Vo D V N 2017 J. Cleaner Prod. 162 170 Google Scholar
[9]	Luo Y R 2007 Comprehensive Handbook of Chemical Bond Energies (Boca Raton: CRC) pp19−342
[10]	Zhang X, Wenren Y, Zhou W, Han J, Lu H, Zhu Z, Wu Z, Cha M S 2020 J. Phys. D: Appl. Phys. 53 194002 Google Scholar
[11]	Brune L, Ozkan A, Genty E, Bocarmé T V, Reniers F 2018 J. Phys. D: Appl. Phys. 51 234002 Google Scholar
[12]	Maqueo P D G, Coulombe S, Bergthorson J M 2019 J. Phys. D: Appl. Phys. 52 274002 Google Scholar
[13]	Alawi N M, Sunarso J, Pham G H, Barifcani A, Nguyen M H, Liu S 2020 J. Ind. Eng. Chem. 85 118 Google Scholar
[14]	王晓玲, 高远, 张帅, 孙昊, 李杰, 邵涛 2019 电工技术学报 34 1329 Wang X L, Gao Y, Zhang S, Sun H, Li J, Shao T 2019 Trans. Chin. Electrotechn. Soc. 34 1329
[15]	Wu A, Yan J, Zhang H, Zhang M, Du C, Li X 2014 Int. J. Hydrogen Energy 39 17656 Google Scholar
[16]	Khoja A H, Tahir M, Amin N A S 2019 Energy Convers. Manage. 183 529 Google Scholar
[17]	王建龙, 丁芳, 朱晓东 2015 64 045206 Google Scholar Wang J L, Ding F, Zhu X D 2015 Acta Phys. Sin. 64 045206 Google Scholar
[18]	赵曰峰, 王超, 王伟宗, 李莉, 孙昊, 邵涛, 潘杰 2018 67 085202 Google Scholar Zhao Y F, Wang C, Wang W Z, Li L, Sun H, Shao T, Pan J 2018 Acta Phys. Sin. 67 085202 Google Scholar
[19]	Slaets J, Aghaei M, Ceulemans S, Van Alphen S, Bogaerts A 2020 Green Chem. 22 1366 Google Scholar
[20]	Wang W, Snoeckx R, Zhang X, Cha M S, Bogaerts A 2018 J. Phys. Chem. C 122 8704 Google Scholar
[21]	Snoeckx R, Aerts R, Tu X, Bogaerts A 2013 J. Phys. Chem. C 117 4957 Google Scholar
[22]	Liu S, Winter L R, Chen J G 2020 ACS Catal. 10 2855 Google Scholar
[23]	Bogaerts A, De Bie C, Snoecks R, Kozak T 2017 Plasma Processes Polym. 14 1600070 Google Scholar
[24]	De Bie C, van Dijk J, Bogaerts A 2015 J. Phys. Chem. C 119 22331 Google Scholar
[25]	Lietz A M, Kushner M J 2016 J. Phys. D: Appl. Phys. 49 425204 Google Scholar
[26]	Aerts R, Martens T, Bogaerts A 2012 J. Phys. Chem. C 116 23257 Google Scholar
[27]	Aerts R, Somers W, Bogaerts A 2015 ChemSusChem 8 702 Google Scholar
[28]	Luo Y C, Lietz A M, Yatom S, Kushner M J, Bruggeman P J 2019 J. Phys. D: Appl. Phys. 52 044003 Google Scholar
[29]	Qian M Y, Zhong W S, Kang J S, Liu S Q, Ren C S, Zhang J L, Wang D Z 2020 Jpn. J. Appl. Phys. 59 066003 Google Scholar
[30]	Brown P N, Byrne G D, Hindmarsh A C 1989 SIAM J. Sci. Stat. Comput. 10 1038 Google Scholar
[31]	Zhang S, Gao Y, Sun H, Bai H, Wang R X, Shao T 2018 J. Phys. D: Appl. Phys. 51 274005 Google Scholar
[32]	Bai C J, Wang L J, Li L, Dong X, Xiao Q H, Liu Z Q, Sun J H, Pan J 2019 AIP Adv. 9 035023 Google Scholar

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