Artificial neural networks with response surface methodology for optimization of selective CO2 hydrogenation using K-promoted iron catalyst in a microchannel reactor

doi:10.1016/j.jcou.2017.11.013

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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, Yong 1; Yang, Gang 2; Wen, Chao 3; Zhang, Lian 4; Sun, Zhi 5
刊名	JOURNAL OF CO2 UTILIZATION
	2018-03-01
卷号	24 页码:10-21
关键词	Anns/rsm Optimization Co2 Hydrogenation Iron-based Catalyst Microchannel Reactor
ISSN号	2212-9820
DOI	10.1016/j.jcou.2017.11.013
文献子类	Article
英文摘要	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.
WOS关键词	FISCHER-TROPSCH SYNTHESIS ; PRODUCT DISTRIBUTION ; ACTIVATED CARBON ; OPERATING-CONDITIONS ; LIQUID PRODUCTS ; LIGHT OLEFINS ; REMOVAL ; ANNS ; RSM ; PERFORMANCE
WOS研究方向	Chemistry ; Engineering
语种	英语
WOS记录号	WOS:000428234500002
资助机构	institute of processes engineering of Chinese Academy of Sciences ; Anpeng energy Co Ltd.
内容类型	期刊论文
源URL	[http://ir.ipe.ac.cn/handle/122111/24172]
专题	过程工程研究所_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
推荐引用方式 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.