Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network
1wt% Of Rhodium (Rh) On Magnesium Oxide (Mgo) Catalyst Have Been Investigated For Carbon Dioxide Reforming Of Methane (CORM) With The Effect Of Oxygen. The Effect Of Temperature,O2/CH4 Ratio And Catalyst Weight On The Methane Conversion, Synthesis Gas Selectivity And H2/CO Ratio Were Studied. With T...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2005
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| Online Access: | http://eprints.utm.my/1700/ http://eprints.utm.my/1700/1/JTDIS43F2.pdf |
| _version_ | 1848890192006479872 |
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| author | Saidina Amin, Nor Aishah Mohd. Yusof, Khairiyah Isha, Ruzinah |
| author_facet | Saidina Amin, Nor Aishah Mohd. Yusof, Khairiyah Isha, Ruzinah |
| author_sort | Saidina Amin, Nor Aishah |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | 1wt% Of Rhodium (Rh) On Magnesium Oxide (Mgo) Catalyst Have Been Investigated For Carbon Dioxide Reforming Of Methane (CORM) With The Effect Of Oxygen. The Effect Of Temperature,O2/CH4 Ratio And Catalyst Weight On The Methane Conversion, Synthesis Gas Selectivity And H2/CO Ratio Were Studied. With The Help Of Experimental Design, Two Mathematical Approaches: Empirical Polynomial And Artificial Neural Network Were Developed. Empirical Polynomial Models Correlation Coefficient, R, Was Above 85%. However, The Feed Forward Neural Network Correlation Coefficient Was More Than 95%. The Feed Forward Neural Network Modeling Approach Was Found To Be More Efficient Than The Empirical Model Approach. The Condition For Maximum Methane Conversion Was Obtained At 850°C With O2/ CH4 Ratio Of 0.14 And 141 Mg Of Catalyst Resulting In 95% Methane Conversion. A Maximum Of 40% Hydrogen Selectivity Was Achieved At 909°C, 0.23 Of O2/CH4 Ratio And 309 Mg Catalyst. The Maximum H2/CO Ratio Of 1.6 Was Attained At 758°C, 0.19 Of O2/CH4 And 360 Mg Catalyst. |
| first_indexed | 2025-11-15T20:38:09Z |
| format | Article |
| id | utm-1700 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:38:09Z |
| publishDate | 2005 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-17002017-11-01T04:17:32Z http://eprints.utm.my/1700/ Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network Saidina Amin, Nor Aishah Mohd. Yusof, Khairiyah Isha, Ruzinah Q Science (General) T Technology (General) 1wt% Of Rhodium (Rh) On Magnesium Oxide (Mgo) Catalyst Have Been Investigated For Carbon Dioxide Reforming Of Methane (CORM) With The Effect Of Oxygen. The Effect Of Temperature,O2/CH4 Ratio And Catalyst Weight On The Methane Conversion, Synthesis Gas Selectivity And H2/CO Ratio Were Studied. With The Help Of Experimental Design, Two Mathematical Approaches: Empirical Polynomial And Artificial Neural Network Were Developed. Empirical Polynomial Models Correlation Coefficient, R, Was Above 85%. However, The Feed Forward Neural Network Correlation Coefficient Was More Than 95%. The Feed Forward Neural Network Modeling Approach Was Found To Be More Efficient Than The Empirical Model Approach. The Condition For Maximum Methane Conversion Was Obtained At 850°C With O2/ CH4 Ratio Of 0.14 And 141 Mg Of Catalyst Resulting In 95% Methane Conversion. A Maximum Of 40% Hydrogen Selectivity Was Achieved At 909°C, 0.23 Of O2/CH4 Ratio And 309 Mg Catalyst. The Maximum H2/CO Ratio Of 1.6 Was Attained At 758°C, 0.19 Of O2/CH4 And 360 Mg Catalyst. Penerbit UTM Press 2005-12 Article PeerReviewed application/pdf en http://eprints.utm.my/1700/1/JTDIS43F2.pdf Saidina Amin, Nor Aishah and Mohd. Yusof, Khairiyah and Isha, Ruzinah (2005) Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network. Jurnal Teknologi F (43F). pp. 15-30. ISSN 0127-9696 http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/784/768 |
| spellingShingle | Q Science (General) T Technology (General) Saidina Amin, Nor Aishah Mohd. Yusof, Khairiyah Isha, Ruzinah Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network |
| title | Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network
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| title_full | Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network
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| title_fullStr | Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network
|
| title_full_unstemmed | Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network
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| title_short | Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network
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| title_sort | carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network |
| topic | Q Science (General) T Technology (General) |
| url | http://eprints.utm.my/1700/ http://eprints.utm.my/1700/ http://eprints.utm.my/1700/1/JTDIS43F2.pdf |