Artificial Neural Network for Combined Steam-Carbon Dioxide Reforming of Methane

被引：1
|
作者：
Jo, Sharon [1 ]
Ma, Byung Chol [2 ,3 ]
Kim, Young Chul [2 ,3 ]
机构：
[1] Chonnam Natl Univ, Dept Chem Engn, 77 Yongbong Ro, Gwangju 500757, South Korea
[2] Chonnam Natl Univ, Sch Chem Engn, 77 Yongbong Ro, Gwangju 61186, South Korea
[3] Chonnam Natl Univ, Res Inst Catalysis, 77 Yongbong Ro, Gwangju 61186, South Korea
来源：
JOURNAL OF NANOSCIENCE AND NANOTECHNOLOGY | 2020年 / 20卷 / 09期
基金：
新加坡国家研究基金会;
关键词：
Methane Reforming; Nickel Catalyst; Modelling; RESPONSE-SURFACE METHODOLOGY; RSM; ANN;
D O I：
10.1166/jnn.2020.17634
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
The CH4 conversion, CO2 conversion, and H-2/CO ratio were set as dependent variables, as the feed rate, flow rate and reaction temperature as independent variables in the complex reaction of methane. We used the Artificial Neural Network (ANN) technique to build a model of the process. The ANN technique was able to predict the reforming process with higher accuracy due to the training capability. The reaction temperature has the greatest effect on the CO2 CH4 reforming reaction. This is because the catalytic reaction temperature has a direct influence on the thermodynamic value and the reaction rate and the equilibrium state.
引用
收藏
页码：5730 / 5733
页数：4
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