Modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and Box-Behnken design

Bamidele V. Ayodele; Chin Kui Cheng

doi:10.1016/j.jiec.2015.08.021

Modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and Box-Behnken design

Bamidele V. Ayodele, Chin Kui Cheng

Chemical and Petroleum Engineering

Universiti Malaysia Pahang

Research output: Contribution to journal › Article › peer-review

75 Scopus citations

Abstract

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 46.85kPa were obtained at 728°C with corresponding conversions of 74.84% and 76.49% for CH₄ and CO₂, respectively.

Original language	British English
Pages (from-to)	246-258
Number of pages	13
Journal	Journal of Industrial and Engineering Chemistry
Volume	32
DOIs	https://doi.org/10.1016/j.jiec.2015.08.021
State	Published - 25 Dec 2015

Keywords

Artificial neural network
Box Behnken design
Ceria
Cobalt
Methane dry reforming
Syngas

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jiec.2015.08.021

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title = "Modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and Box-Behnken design",

abstract = "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 46.85kPa were obtained at 728°C with corresponding conversions of 74.84% and 76.49% for CH4 and CO2, respectively.",

keywords = "Artificial neural network, Box Behnken design, Ceria, Cobalt, Methane dry reforming, Syngas",

author = "Ayodele, {Bamidele V.} and Cheng, {Chin Kui}",

note = "Funding Information: The authors would like to acknowledge the research fund RDU130501 granted by the Ministry of Science, Technology and Innovation, Malaysia (MOSTI) and the DSS scholarship granted to the first author by Universiti Malaysia Pahang . Publisher Copyright: {\textcopyright} 2015 The Korean Society of Industrial and Engineering Chemistry.",

year = "2015",

month = dec,

day = "25",

doi = "10.1016/j.jiec.2015.08.021",

language = "British English",

volume = "32",

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journal = "Journal of Industrial and Engineering Chemistry",

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publisher = "Korean Society of Industrial Engineering Chemistry",

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T1 - Modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and Box-Behnken design

AU - Ayodele, Bamidele V.

AU - Cheng, Chin Kui

N1 - Funding Information: The authors would like to acknowledge the research fund RDU130501 granted by the Ministry of Science, Technology and Innovation, Malaysia (MOSTI) and the DSS scholarship granted to the first author by Universiti Malaysia Pahang . Publisher Copyright: © 2015 The Korean Society of Industrial and Engineering Chemistry.

PY - 2015/12/25

Y1 - 2015/12/25

N2 - 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 46.85kPa were obtained at 728°C with corresponding conversions of 74.84% and 76.49% for CH4 and CO2, respectively.

AB - 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 46.85kPa were obtained at 728°C with corresponding conversions of 74.84% and 76.49% for CH4 and CO2, respectively.

KW - Artificial neural network

KW - Box Behnken design

KW - Ceria

KW - Cobalt

KW - Methane dry reforming

KW - Syngas

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U2 - 10.1016/j.jiec.2015.08.021

DO - 10.1016/j.jiec.2015.08.021

M3 - Article

AN - SCOPUS:84948711055

SN - 1226-086X

VL - 32

SP - 246

EP - 258

JO - Journal of Industrial and Engineering Chemistry

JF - Journal of Industrial and Engineering Chemistry

ER -

Modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and Box-Behnken design

Abstract

Keywords

UN SDGs

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