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Multiscale CFD Simulation of an Industrial Diameter-Transformed Fluidized Bed Reactor with Artificial Neural Network Analysis of EMMS Drag Markers

文献类型：期刊论文

作者Du, Chengzhe2,3; Han, Caixia2,3; Yang, Zhuo2,3; Wu, Hao1,3; Luo, Hao4; Niedzwiecki, Lukasz5; Lu, Bona2,3; Wang, Wei2,3
刊名INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
出版日期2022-06-22
卷号61期号:24页码:8566-8580
ISSN号0888-5885
DOI10.1021/acs.iecr.2c00396
英文摘要Modeling of gas-solid, heterogeneously catalytic, diameter-transformed fluidized bed (DTFB) reactors is intrinsically complex and requires considering the variation of material properties and operating conditions, because of reactions and/or diameter transformation. The EMMS-matrix drag model, which correlates both operating conditions and local parameters, has been applied in computational fluid dynamics (CFD) simulation of such complex reactors by simplifying the macroscale operating conditions with one set of constant parameters. However, a complete scheme has not been reported that covers a wide range of datasets for a DTFB reactor with complex reactions. To this end, the artificial neural network (ANN), which enables exploring a multivariate relation with the contribution of a set of different parameters, is chosen to couple with EMMS drag modeling. A complete scheme of EMMS-ANN drag for hot, reactive simulation of DTFB is thereby established, with comprehensive evaluation of the contribution of drag markers successively considering the variation of gas properties and operating parameters. Both a priori tests and CFD simulations show that the voidage and slip velocity are the dominant factors in modeling of drag correction, and the effects of dynamic variation of gas properties and operating hydrodynamics are marginal; even the heterogeneous reactions and the change in bed diameter give rise to a remarkable variation in gas properties and operating parameters. The underlying mechanism is then analyzed to provide important dues for drag modeling of gas-solid, heterogeneous catalytic fluidized-bed reactors.
WOS关键词SOLID 2-PHASE FLOW ; EULERIAN SIMULATION ; MODEL ; GELDART ; RISER ; PARTICLES ; FEATURES
资助项目National Natural Science Foundation of China[22078331] ; National Natural Science Foundation of China[22161142006] ; "Transformational Technologies for Clean Engery and Demonstration", Strategic Priority Research Program of the Chinese Academy of Sciences[XDA21030700]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000815843000001
出版者AMER CHEMICAL SOC
资助机构National Natural Science Foundation of China ; "Transformational Technologies for Clean Engery and Demonstration", Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.ipe.ac.cn/handle/122111/53982]  
专题中国科学院过程工程研究所
通讯作者Lu, Bona
作者单位1.Tech Univ Denmark, Dept Chem & Biochem Engn, DK-2800 Lyngby, Denmark
2.Chinese Acad Sci, Inst Proc Engn, State Key Lab Multiphase Complex Syst, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Wuhan Univ Sci & Technol, Sch Chem & Chem Engn, Wuhan 430081, Hubei, Peoples R China
5.Wroclaw Univ Sci & Technol, Dept Mech Machines Devices & Energy Proc, PL-50370 Wroclaw, Poland
推荐引用方式
GB/T 7714		 Du, Chengzhe,Han, Caixia,Yang, Zhuo,et al. Multiscale CFD Simulation of an Industrial Diameter-Transformed Fluidized Bed Reactor with Artificial Neural Network Analysis of EMMS Drag Markers[J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,2022,61(24):8566-8580.
APA Du, Chengzhe.,Han, Caixia.,Yang, Zhuo.,Wu, Hao.,Luo, Hao.,...&Wang, Wei.(2022).Multiscale CFD Simulation of an Industrial Diameter-Transformed Fluidized Bed Reactor with Artificial Neural Network Analysis of EMMS Drag Markers.INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,61(24),8566-8580.
MLA Du, Chengzhe,et al."Multiscale CFD Simulation of an Industrial Diameter-Transformed Fluidized Bed Reactor with Artificial Neural Network Analysis of EMMS Drag Markers".INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 61.24(2022):8566-8580.

入库方式： OAI收割

来源：过程工程研究所

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