Modelling and optimization of catalytic-dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network - genetic algorithm technique
A hybrid artificial neural network-genetic algorithm (ANN-GA) was developed to model, simulate and optimize the catalytic-dielectric barrier discharge plasma reactor. Effects of CH4/CO2 feed ratio, total feed flow rate, discharge voltage and reactor wall temperature on the performance of the reactor...
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| Main Authors: | , |
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| Format: | Article |
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Pergamon-Elsevier Science Ltd
2007
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/8704/ http://dx.doi.org/10.1016/j.ces.2007.07.066 |
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| Summary: | A hybrid artificial neural network-genetic algorithm (ANN-GA) was developed to model, simulate and optimize the catalytic-dielectric barrier discharge plasma reactor. Effects of CH4/CO2 feed ratio, total feed flow rate, discharge voltage and reactor wall temperature on the performance of the reactor was investigated by the ANN-based model simulation. Pareto optimal solutions and the corresponding optimal operating parameter range based on multi-objective scan be suggested for two cases, i.e., simultaneous maximization of CH4 conversion and C2+ selectivity (Case 1), and H-2 selectivity and H-2/CO ratio (Case 2). It can be concluded that the hybrid catalytic-dielectric barrier discharge plasma reactor is potential for co-generation of synthesis gas and higher hydrocarbons from methane and carbon dioxide and performed better than the conventional fixed-bed reactor with respect to CH4 conversion, C2+ yield and H-2 selectivity. |
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