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A machine learning approach for electrical capacitance tomography measurement of gas-solid fluidized beds

文献类型：期刊论文

作者Guo, Qiang1,2,3; Ye, Mao1,2; Yang, Wuqiang4; Liu, Zhongmin1,2
刊名AICHE JOURNAL
出版日期2019-06-01
卷号65期号:6页码:18
关键词electrical capacitance tomography fluidized bed high-throughput experiment machine learning on-line monitoring
ISSN号0001-1541
DOI10.1002/aic.16583
通讯作者Ye, Mao(maoye@dicp.ac.cn)
英文摘要Electrical capacitance tomography has been widely used to obtain key hydrodynamic parameters of gas-solid fluidized beds, which is normally realized by first reconstructing images and then by analyzing these images. This indirect approach is time-consuming and hence difficult for on-line monitoring. Meanwhile, considering recurrence of similar flow patterns in fluidized beds, most of these calculations are repetitive and should be avoided. Here, we develop a machine learning approach to address these problems. First, superficial gas velocity linear-increasing strategy is used to perform high-throughput experiments to collect a large amount of training samples. These samples are used to train the map from normalized capacitance measurements to key parameters that obtained by an iterative image reconstruction algorithm off-line. The trained model can then be used for on-line monitoring. Preliminary tests revealed that the trained models show good prediction and generality for the estimation of the overall solid concentration and the equivalent bubble diameter.
WOS关键词IMAGE-RECONSTRUCTION ALGORITHM ; VOLUME TOMOGRAPHY ; GELDART ; PARTICLES ; DIAMETER ; REGIME ; VERIFICATION ; SIMULATION ; DYNAMICS ; BEHAVIOR
资助项目National Key Research and Development Program of China[2018YFB0604904] ; Newton Advanced Fellowship of the Royal Society, UK[NA140308]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000467752600003
出版者WILEY
资助机构National Key Research and Development Program of China ; National Key Research and Development Program of China ; Newton Advanced Fellowship of the Royal Society, UK ; Newton Advanced Fellowship of the Royal Society, UK ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Newton Advanced Fellowship of the Royal Society, UK ; Newton Advanced Fellowship of the Royal Society, UK ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Newton Advanced Fellowship of the Royal Society, UK ; Newton Advanced Fellowship of the Royal Society, UK ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Newton Advanced Fellowship of the Royal Society, UK ; Newton Advanced Fellowship of the Royal Society, UK
源URL[http://cas-ir.dicp.ac.cn/handle/321008/171869]  
专题大连化学物理研究所_中国科学院大连化学物理研究所
通讯作者Ye, Mao
作者单位1.Chinese Acad Sci, Dalian Natl Lab Clean Energy, Dalian Inst Chem Phys, Dalian 116023, Peoples R China
2.Chinese Acad Sci, Natl Engn Lab MTO, Dalian Inst Chem Phys, Dalian 116023, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Univ Manchester, Sch Elect & Elect Engn, Manchester, Lancs, England
推荐引用方式
GB/T 7714		 Guo, Qiang,Ye, Mao,Yang, Wuqiang,et al. A machine learning approach for electrical capacitance tomography measurement of gas-solid fluidized beds[J]. AICHE JOURNAL,2019,65(6):18.
APA Guo, Qiang,Ye, Mao,Yang, Wuqiang,&Liu, Zhongmin.(2019).A machine learning approach for electrical capacitance tomography measurement of gas-solid fluidized beds.AICHE JOURNAL,65(6),18.
MLA Guo, Qiang,et al."A machine learning approach for electrical capacitance tomography measurement of gas-solid fluidized beds".AICHE JOURNAL 65.6(2019):18.

入库方式： OAI收割

来源：大连化学物理研究所

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