Electrical Capacitance Volume Tomography: a Comparison Between 12- and 24-Channels Sensor Systems

Spatial resolution represents a key performance aspect in electrical capacitance volume tomography (ECVT) systems. Factors affecting the resolution include the "soft-field" nature of ECVT, the number of capacitance channels used, the ill-conditioned nature of the imaging reconstruction problem, and the signal-to-noise ratio of the measurement apparatus. In this study, the effect of choosing different numbers of capacitance plates on the performance of ECVT is investigated experimentally. Specifically, two ECVT sensors with 12 and 24 capacitance channels but covering equal volumes of a cylinder are used to examine the resulting impact on the image resolution.

[1]  Liang-Shih Fan,et al.  A Multimodal Tomography System Based on ECT Sensors , 2007, IEEE Sensors Journal.

[2]  Jarkko Ketolainen,et al.  Electrical capacitance tomography as a monitoring tool for high-shear mixing and granulation , 2011 .

[3]  Lijun Xu,et al.  Image reconstruction technique of electrical capacitance tomography for low-contrast dielectrics using Calderon's method , 2009 .

[4]  F. Teixeira,et al.  A nonlinear image reconstruction technique for ECT using a combined neural network approach , 2006 .

[5]  Liang-Shih Fan,et al.  Electrical Capacitance Volume Tomography , 2007, IEEE Sensors Journal.

[6]  Fei Wang,et al.  Electrical Capacitance Volume Tomography Imaging of Three-Dimensional Flow Structures and Solids Concentration Distributions in a Riser and a Bend of a Gas–Solid Circulating Fluidized Bed , 2012 .

[7]  Phaneendra K. Yalavarthy,et al.  Helmholtz-Type Regularization Method for Permittivity Reconstruction Using Experimental Phantom Data of Electrical Capacitance Tomography , 2010, IEEE Transactions on Instrumentation and Measurement.

[8]  Wuqiang Yang,et al.  Scale-up of an electrical capacitance tomography sensor for imaging pharmaceutical fluidized beds and validation by computational fluid dynamics , 2011 .

[9]  Yuri A. Sergeev,et al.  EXPERIMENTAL OBSERVATIONS OF VOIDAGE DISTRIBUTION AROUND BUBBLES IN A FLUIDIZED BED , 1994 .

[10]  Martin E. Weber,et al.  Bubbles in viscous liquids: shapes, wakes and velocities , 1981, Journal of Fluid Mechanics.

[11]  Chuanlong Xu,et al.  Thick-wall electrical capacitance tomography and its application in dense-phase pneumatic conveying under high pressure , 2011 .

[12]  Q. Marashdeh,et al.  Nonlinear forward problem solution for electrical capacitance tomography using feed-forward neural network , 2006, IEEE Sensors Journal.

[13]  Fernando L. Teixeira,et al.  Adaptive Electrical Capacitance Volume Tomography , 2014, IEEE Sensors Journal.

[14]  Liang-Shih Fan,et al.  Neural network multi-criteria optimization image reconstruction technique (NN-MOIRT) for linear and non-linear process tomography , 2003 .

[15]  Fei Wang,et al.  Electrical Capacitance Volume Tomography: Design and Applications , 2010, Sensors.

[16]  Jiamin Ye,et al.  Evaluation of Effect of Number of Electrodes in ECT Sensors on Image Quality , 2012, IEEE Sensors Journal.

[17]  J. S. Dennis,et al.  Magnetic resonance studies of jets in a gas–solid fluidised bed , 2012 .

[18]  Wuqiang Yang,et al.  Design of electrical capacitance tomography sensors , 2010 .

[19]  F. Teixeira,et al.  Sensitivity matrix calculation for fast 3-D electrical capacitance tomography (ECT) of flow systems , 2004, IEEE Transactions on Magnetics.

[20]  J. S. Dennis,et al.  A comparison of magnetic resonance imaging and electrical capacitance tomography: An air jet through a bed of particles , 2012 .

[21]  Fernando L. Teixeira,et al.  Sensitivity map computation in adaptive electrical capacitance volume tomography with multielectrode excitations , 2015 .

[22]  Fernando L. Teixeira,et al.  Dual imaging modality of granular flow based on ECT sensors , 2008 .

[23]  J. Lei,et al.  An image reconstruction algorithm based on the semiparametric model for electrical capacitance tomography , 2011, Comput. Math. Appl..