期刊
APPLIED CATALYSIS B-ENVIRONMENTAL
卷 183, 期 -, 页码 124-132出版社
ELSEVIER
DOI: 10.1016/j.apcatb.2015.10.013
关键词
Plasma-catalysis; Dielectric barrier discharge; Methanol removal; Artificial neural network; Environmental clean-up
资金
- UK EPSRC Bioenergy Challenge Programme [EP/M013162/1]
- National Natural Science Foundation of China [51076140, 51206143]
- National Science Fund for Distinguished Young Scholars [51125025]
- Engineering and Physical Sciences Research Council [EP/M013162/1] Funding Source: researchfish
- EPSRC [EP/M013162/1] Funding Source: UKRI
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. (C) 2015 Elsevier B.V. All rights reserved.
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