Universiti Teknologi Malaysia Institutional Repository

Carbon dioxide reforming of methane to syngas: modeling using response surface methodology and artificial neural network

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

[img] PDF
597Kb

Official URL: http://www.penerbit.utm.my/cgi-bin/jurnal/artikel....

Abstract

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.

Item Type:Article
Uncontrolled Keywords:synthesis gas, carbon dioxide reforming of methane, rhodium, mgo, experimental design, feed forward neural network
Subjects:Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions:Chemical and Natural Resources Engineering (Formerly known)
ID Code:1700
Deposited By: En Mohd. Nazir Md. Basri
Deposited On:19 Mar 2007 06:53
Last Modified:06 Jun 2012 03:48

Repository Staff Only: item control page