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Post-plasma catalytic removal of methanol over Mn–Ce catalysts in an atmospheric dielectric barrier discharge

Paper ID Volume ID Publish Year Pages File Format Full-Text
45140 46401 2016 9 PDF Available
Title
Post-plasma catalytic removal of methanol over Mn–Ce catalysts in an atmospheric dielectric barrier discharge
Abstract

•Methanol can be effectively removed using a post-plasma catalysis system.•Mn50Ce50 catalyst exhibited the best performance among the tested Mn–Ce catalysts.•Reaction mechanisms and pathways of post-plasma catalytic removal of methanol are proposed.•The importance of individual processing parameter has been identified.•ANN can be used to predict the performance of the complex plasma-catalytic process.

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.

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Keywords
Plasma-catalysis; Dielectric barrier discharge; Methanol removal; Artificial neural network; Environmental clean-up
First Page Preview
Post-plasma catalytic removal of methanol over Mn–Ce catalysts in an atmospheric dielectric barrier discharge
Publisher
Database: Elsevier - ScienceDirect
Journal: Applied Catalysis B: Environmental - Volume 183, April 2016, Pages 124–132
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Catalysis