Artificial neural networks with response surface methodology for optimization of selective CO2 hydrogenation using K-promoted iron catalyst in a microchannel reactor
Sun, Yong1; Yang, Gang2; Wen, Chao3; Zhang, Lian4; Sun, Zhi5
2018-03-01
发表期刊JOURNAL OF CO2 UTILIZATION
ISSN2212-9820
卷号24页码:10-21
摘要CO2 hydrogenation was optimized by a combination of AANs (Artificial Neuron Networks) with RSM (Response Surface Methodology) in a microchannel reactor using a K-promoted iron-based catalyst. This robust and cost-effective methodology was reliable to extensively analyze the effect of operating conditions i.e. gas ratio, temperature, pressure, and space velocity on product distribution of selective CO2 hydrogenation. With experimental data as training data using ANNs and Box-Behnken design as design of experiment, the obtained model was able to present good results in a nonlinear noisy process with significant changes of critical operation parameters in an experimental design plan during CO2 hydrogenation using K-promoted iron-based catalyst in a microchannel reactor. The achieved quadratic model was flexible and effective in optimizing either single or multiple objections of product distribution for CO2 hydrogenation.
关键词Anns/rsm Optimization Co2 Hydrogenation Iron-based Catalyst Microchannel Reactor
文章类型Article
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1016/j.jcou.2017.11.013
收录类别SCI
语种英语
关键词[WOS]FISCHER-TROPSCH SYNTHESIS ; PRODUCT DISTRIBUTION ; ACTIVATED CARBON ; OPERATING-CONDITIONS ; LIQUID PRODUCTS ; LIGHT OLEFINS ; REMOVAL ; ANNS ; RSM ; PERFORMANCE
WOS研究方向Chemistry ; Engineering
WOS类目Chemistry, Multidisciplinary ; Engineering, Chemical
项目资助者institute of processes engineering of Chinese Academy of Sciences ; Anpeng energy Co Ltd.
WOS记录号WOS:000428234500002
引用统计
文献类型期刊论文
条目标识符http://ir.ipe.ac.cn/handle/122111/24172
专题湿法冶金清洁生产技术国家工程实验室
作者单位1.Edith Cowan Univ, Sch Engn, 270 Joondalup Dr, Joondalup, WA 6027, Australia
2.Anpeng High Tech Energy Corp, Beijing, Peoples R China
3.Northwest Univ, Res Ctr Intelligent Interact & Informat Art, Xian 710069, Shaanxi, Peoples R China
4.Monash Univ, Dept Chem Engn, Clayton, Vic 3800, Australia
5.Chinese Acad Sci, Inst Proc Engn, Natl Engn Lab Hydromet Cleaner Prod Technol, Beijing 100190, Peoples R China
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GB/T 7714
Sun, Yong,Yang, Gang,Wen, Chao,et al. Artificial neural networks with response surface methodology for optimization of selective CO2 hydrogenation using K-promoted iron catalyst in a microchannel reactor[J]. JOURNAL OF CO2 UTILIZATION,2018,24:10-21.
APA Sun, Yong,Yang, Gang,Wen, Chao,Zhang, Lian,&Sun, Zhi.(2018).Artificial neural networks with response surface methodology for optimization of selective CO2 hydrogenation using K-promoted iron catalyst in a microchannel reactor.JOURNAL OF CO2 UTILIZATION,24,10-21.
MLA Sun, Yong,et al."Artificial neural networks with response surface methodology for optimization of selective CO2 hydrogenation using K-promoted iron catalyst in a microchannel reactor".JOURNAL OF CO2 UTILIZATION 24(2018):10-21.
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