globalchange  > 气候减缓与适应
DOI: 10.1016/j.jcou.2017.11.013
Scopus记录号: 2-s2.0-85037527022
论文题名:
Artificial neural networks with response surface methodology for optimization of selective CO2 hydrogenation using K-promoted iron catalyst in a microchannel reactor
作者: Sun Y.; Yang G.; Wen C.; Zhang L.; Sun Z.
刊名: Journal of CO2 Utilization
ISSN: 22129820
出版年: 2018
卷: 24
起始页码: 10
结束页码: 21
语种: 英语
英文关键词: ANNs/RSM ; CO2 hydrogenation ; Iron-based catalyst ; Microchannel reactor ; Optimization
Scopus关键词: Carbon dioxide ; Catalysts ; Cost effectiveness ; Design of experiments ; Iron ; Microchannels ; Neural networks ; Optimization ; Surface properties ; ANNs/RSM ; Artificial neuron networks ; CO2 hydrogenation ; Critical operations ; Iron-based catalyst ; Micro channel reactors ; Product distributions ; Response surface methodology ; Hydrogenation
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. © 2017 Elsevier Ltd
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111958
Appears in Collections:气候减缓与适应

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作者单位: Edith Cowan University School of Engineering, 270 Joondalup Drive, Joondalup, WA 6027, Australia; Anpeng High-tech Energy Corp, Beijing, China; Northwest university, Research Center for Intelligent Interaction and Information Art710069, China; Monash University Department of Chemical Engineering, VIC, 3800, Australia; National Engineering Laboratory for Hydrometallurgical Cleaner Production Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China

Recommended Citation:
Sun Y.,Yang G.,Wen C.,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,2018-01-01,24
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