Reconstruction algorithm of electrical impedance tomography for particle concentration distribution in suspension

被引:4
|
作者
Kim, MC [1 ]
Kim, KY
Kim, S
Lee, KJ
机构
[1] Cheju Natl Univ, Dept Chem Engn, Cheju 690756, South Korea
[2] Cheju Natl Univ, Dept Elect & Elect Engn, Cheju 690756, South Korea
[3] Cheju Natl Univ, Dept Nucl & Energy Engn, Cheju 690756, South Korea
基金
新加坡国家研究基金会;
关键词
particle concentration; electrical impedance tomography; complete electrode model; inverse crime; regularization;
D O I
10.1007/BF02705419
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
An inverse problem is solved to obtain the particle concentration profile in suspension under pressuredriven flow with electrical impedance tomography (EIT). The finite element method (FEM) is employed in the forward problem and the regularized Newton-Raphson iterative method is used in the inverse problem. Different FEM meshes are used in the forward and the inverse problem not to commit inverse crime. To avoid post-calibration of measurement data, the complete electrode model is introduced. For the evaluation of the robustness of the reconstruction algorithm, several testing cases with measurement error are considered. The proposed algorithm can be used to reconstruct the particle concentration in suspension.
引用
收藏
页码:352 / 357
页数:6
相关论文
共 50 条
  • [41] An Image Reconstruction Algorithm for Electrical Impedance Tomography Using Measurement Estimation of Virtual Electrodes
    Yang, Lu
    Wu, Hongtao
    Liu, Kai
    Chen, Bai
    Yang, Yunjie
    Zhu, Chengjun
    Yao, Jiafeng
    IEEE SENSORS JOURNAL, 2022, 22 (13) : 13012 - 13022
  • [42] Target Adaptive Differential Iterative Reconstruction (TADI): A Robust Algorithm for Real-Time Electrical Impedance Tomography
    Zhang, Weirui
    Zhang, Tao
    Liu, Xuechao
    Yang, Bin
    Dai, Meng
    Shi, Xuetao
    Dong, Xiuzhen
    Fu, Feng
    Xu, Canhua
    IEEE ACCESS, 2021, 9 : 141999 - 142011
  • [43] MONOTONICITY-BASED RECONSTRUCTION OF EXTREME INCLUSIONS IN ELECTRICAL IMPEDANCE TOMOGRAPHY
    Candiani, Valentina
    Darde, Jeremi
    Garde, Henrik
    Hyvonen, Nuutti
    SIAM JOURNAL ON MATHEMATICAL ANALYSIS, 2020, 52 (06) : 6234 - 6259
  • [44] Quantification of measurement error effects on conductivity reconstruction in electrical impedance tomography
    Sun, Xiang
    Lee, Eunjung
    Choi, Jung-Il
    INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, 2020, 28 (12) : 1669 - 1693
  • [45] Regularized reconstruction in electrical impedance tomography using a variance uniformization constraint
    CohenBacrie, C
    Goussard, Y
    Guardo, R
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 1997, 16 (05) : 562 - 571
  • [46] Image Reconstruction for Electrical Impedance Tomography Using Radial Basis Function Neural Network Based on Hybrid Particle Swarm Optimization Algorithm
    Wang, Huan
    Liu, Kai
    Wu, Yang
    Wang, Song
    Zhang, Zheng
    Li, Fang
    Yao, Jiafeng
    IEEE SENSORS JOURNAL, 2021, 21 (02) : 1926 - 1934
  • [47] Sparse optimization for image reconstruction in Electrical Impedance Tomography
    Varanasi, Santhosh Kumar
    Manchikatla, Chaitanya
    Polisetty, Venkata Goutham
    Jampana, Phanindra
    IFAC PAPERSONLINE, 2019, 52 (01): : 34 - 39
  • [48] Supershape augmented reconstruction method for electrical impedance tomography
    Gu, Danping
    Liu, Dong
    Deng, Jiansong
    Du, Jiangfeng
    2021 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC 2021), 2021,
  • [49] Block-sparse Reconstruction for Electrical Impedance Tomography
    Wang Qi
    Zhang Pengcheng
    Wang Jianming
    Li Xiuyan
    Lian Zhijie
    Chen Qingliang
    Chen Tongyun
    Chen Xiaojing
    He Jing
    Duan Xiaojie
    Wang Huaxiang
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2018, 40 (03) : 676 - 682
  • [50] A new image reconstruction method for electrical impedance tomography
    Hou, WD
    Mo, WL
    BIOMEDICAL PHOTONICS AND OPTOELECTRONIC IMAGING, 2000, 4224 : 64 - 67