Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design

In the present study, synthesis gas was produced from dry reforming of methane over ceria supported cobalt catalyst in a fixed bed stainless steel reactor. Artificial neural network (ANN) and Box Behnken design (BBD) were employed to investigate the effects of reactant partial pressures, reactant fe...

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Main Authors: Ayodele, Bamidele V., Cheng, C. K.
Format: Article
Published: Elsevier 2015
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/11581/
http://umpir.ump.edu.my/id/eprint/11581/
http://umpir.ump.edu.my/id/eprint/11581/
id ump-11581
recordtype eprints
spelling ump-115812019-08-28T07:04:50Z http://umpir.ump.edu.my/id/eprint/11581/ Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design Ayodele, Bamidele V. Cheng, C. K. TP Chemical technology In the present study, synthesis gas was produced from dry reforming of methane over ceria supported cobalt catalyst in a fixed bed stainless steel reactor. Artificial neural network (ANN) and Box Behnken design (BBD) were employed to investigate the effects of reactant partial pressures, reactant feed ratios, reaction temperature and their optimum conditions. Good agreement was shown between the predicted outputs from the ANN model and the experimental data. Optimum reactant feed ratio of 0.60 and CH4 partial pressure of 46.85 kPa were obtained at 728 °C with corresponding conversions of 74.84% and 76.49% for CH4 and CO2, respectively. Elsevier 2015 Article PeerReviewed Ayodele, Bamidele V. and Cheng, C. K. (2015) Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design. Journal of Industrial and Engineering Chemistry, 32. pp. 246-258. ISSN 1226-086X http://dx.doi.org/10.1016/j.jiec.2015.08.021 doi:10.1016/j.jiec.2015.08.021
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
topic TP Chemical technology
spellingShingle TP Chemical technology
Ayodele, Bamidele V.
Cheng, C. K.
Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design
description In the present study, synthesis gas was produced from dry reforming of methane over ceria supported cobalt catalyst in a fixed bed stainless steel reactor. Artificial neural network (ANN) and Box Behnken design (BBD) were employed to investigate the effects of reactant partial pressures, reactant feed ratios, reaction temperature and their optimum conditions. Good agreement was shown between the predicted outputs from the ANN model and the experimental data. Optimum reactant feed ratio of 0.60 and CH4 partial pressure of 46.85 kPa were obtained at 728 °C with corresponding conversions of 74.84% and 76.49% for CH4 and CO2, respectively.
format Article
author Ayodele, Bamidele V.
Cheng, C. K.
author_facet Ayodele, Bamidele V.
Cheng, C. K.
author_sort Ayodele, Bamidele V.
title Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design
title_short Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design
title_full Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design
title_fullStr Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design
title_full_unstemmed Modelling and Optimization of Syngas Production from Methane Dry Reforming Over Ceria-Supported Cobalt Catalyst Using Artificial Neural Networks and Box–Behnken Design
title_sort modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and box–behnken design
publisher Elsevier
publishDate 2015
url http://umpir.ump.edu.my/id/eprint/11581/
http://umpir.ump.edu.my/id/eprint/11581/
http://umpir.ump.edu.my/id/eprint/11581/
first_indexed 2023-09-18T22:12:29Z
last_indexed 2023-09-18T22:12:29Z
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