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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
Source PublicationJOURNAL OF CO2 UTILIZATION
ISSN2212-9820
Volume24Pages:10-21
AbstractCO2 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.
KeywordAnns/rsm Optimization Co2 Hydrogenation Iron-based Catalyst Microchannel Reactor
SubtypeArticle
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1016/j.jcou.2017.11.013
Indexed BySCI
Language英语
WOS KeywordFISCHER-TROPSCH SYNTHESIS ; PRODUCT DISTRIBUTION ; ACTIVATED CARBON ; OPERATING-CONDITIONS ; LIQUID PRODUCTS ; LIGHT OLEFINS ; REMOVAL ; ANNS ; RSM ; PERFORMANCE
WOS Research AreaChemistry ; Engineering
WOS SubjectChemistry, Multidisciplinary ; Engineering, Chemical
Funding Organizationinstitute of processes engineering of Chinese Academy of Sciences ; Anpeng energy Co Ltd.
WOS IDWOS:000428234500002
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/24172
Collection湿法冶金清洁生产技术国家工程实验室
Affiliation1.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
Recommended Citation
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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