Zhu, Xinbo, Liu, Shiyun 
ORCID: 0000-0002-7014-3002, Cai, Yuxiang, Gao, Xiang, Zhou, Jinsong, Zheng, Chenghang and Tu, Xin ORCID: 0000-0002-6376-0897
(2016)
Post-plasma catalytic removal of methanol over Mn-Ce catalysts in an atmospheric dielectric barrier discharge.
APPLIED CATALYSIS B-ENVIRONMENTAL, 183.
pp. 124-132.
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Abstract
A post-plasma catalysis system has been developed for the removal of methanol over Mn-Ce oxide catalysts with different Mn/Ce molar ratios at low temperatures. The Mn50Ce50 oxide catalyst (Mn/Ce = 1:1) shows the best performance in terms of methanol removal efficiency and energy efficiency of the plasma-catalytic process. The maximum methanol removal efficiency of 95.4% can be achieved at a discharge power of 15 W and a gas flow rate of 1 L/min, while the highest energy efficiency of the plasma-catalytic process is 47.5 g/kW h at 1.9 W. The combination of plasma and Mn-Ce catalysts significantly reduces the formation of major by-products (methane, formaldehyde and formic acid) based on the Fourier transform infrared spectra. Possible reaction mechanisms and pathways of the post-plasma catalytic removal of methanol are also proposed. A three-layer back propagation artificial neural network (ANN) model has been developed to get a better understanding of the roles of different process parameters on methanol removal efficiency and energy efficiency in the post-plasma catalytic process. The predicted data from the ANN model show a good agreement with the experimental results. Catalyst composition (i.e. Mn/Ce ratio) is found to be the most important factor affecting methanol removal efficiency with a relative importance of 31.53%, while the discharge power is the most influential parameter for energy efficiency with a relative weight of 30.40%. These results indicate that the well-trained ANN model provides an alternative approach for accurate and fast prediction of the plasma-catalytic chemical reactions.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Plasma-catalysis, Dielectric barrier discharge, Methanol removal, Artificial neural network, Environmental clean-up |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 27 Apr 2016 13:05 |
| Last Modified: | 19 Jan 2023 07:37 |
| DOI: | 10.1016/j.apcatb.2015.10.013 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3000943 |
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