Proper normalization of the measured capacitance data is a prerequisite in electrical capacitance tomography (ECT). Conventional methods, such as parallel and series normalization, assume a higher permittivity perturbation in a homogeneous (lower) permittivity background. Although this is applicable to a number of cases, some two-phase flows are better modeled instead as a lower permittivity perturbation in a homogeneous (higher) permittivity background. A different normalization method is proposed for such cases, which provides similar image quality as conventional but is particularly beneficial for velocimetry applications based on sensitivity gradient of the ECT sensor. Simulation and experimental results are provided to illustrate the advantages for the proposed normalization technique.
We describe an approach, based on electrical capacitance tomography (ECT) sensors, to decompose and continuously monitor multiphase ﬂow components (fractional areas or volumes) in mixtures containing conducting phases. The proposed approach exploits the Maxwell-Wagner-Sillars (MWS) effect at distinct frequencies to reconstruct each phase of a multiphase ﬂow and is also utilized to estimate the fractional volume of the various phases of the mixture. The approach is illustrated for a three-phase mixture composed of air, water and oil. This approach utilizes the very same ECT measurement apparatus used for ﬂow imaging and, as such, inherits its high speed of acquisition and suitability for real-time operation.
We compare electrical capacitance tomography (ECT) and displacement-current phase tomography (DCPT) results for non-invasive imaging of lossy media. ECT is based on mutual capacitance measurements between electrode pairs surrounding the region of interest (RoI), whereas DCPT is a relatively less mature sensing modality that utilizes the phase information inherent in the displacement current measured by such electrode pairs excited by time-harmonic voltages (in the electroquasistatic regime). DCPT and ECT can be implemented using basically the same hardware components and used along-side to provide complementary information for imaging purposes or separately to reconstruct the spatial distribution of the loss tangent or the permittivity within the RoI, respectively. We show that the (nonlinear) relationship between the measured phase in DCPT and the conductivity distribution in the RoI has a more extended linear range than the nonlinear relationship between the measure capacitances in ECT and the permittivity distribution in the RoI. Of note, DCPT does not require electrical contact with the RoI in contrast to electrical impedance tomography (EIT). To illustrate the potential of DCPT, we evaluate its performance using both numerical examples and experiment results.
We introduce a method based on the Maxwell-Wagner-Sillars (MWS) effect to improve the performance of Displacement-Current Phase Tomography (DCPT) applied to two-phase flow imaging. DCPT utilizes as set of mutual admittance measurements between electrodes placed around a region of interest (RoI). This measurement can extract the phase of the displacement current between the electrodes so as to characterize the spatial distribution of the conductivity or dielectric loss inside the RoI. By exploiting the fact that the measured data at different frequencies will exhibit distinct MWS effects, the proposed approach can extract additional information from the measure data set and improve the resolution of DCPT for the imaging of two-phase flows. Numerical simulations along with experimental results illustrate the main findings of this work.
Velocity profiling of a flow involves the task of determining the velocity vector at every point in a given flow volume. A new method is proposed for velocity profiling of multiphase flows based on electrical capacitance volume tomography (ECVT) sensors. The proposed method utilizes a mapping between the change in measured capacitances and the displacement of flow that is effected by the spatial gradient of the sensitivity distribution. This novel mapping not only avoids the need for costly image cross-correlations but also is fully compatible with existing ECVT sensor and image reconstruction algorithms. Simulation and measurement results are provided to demonstrate the proposed method.
Electrical Capacitance Volume Tomography: A Comparison between 12- and 24-Channels Sensor Systems
Progress In Electromagnetics Research M Vol. 41, 73–84, 2015
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.
Electrical capacitance tomography (ECT) is a low-cost, high-speed imaging technique useful in many industrial settings. ECT is predicated on the knowledge of sensitivity maps between capacitance electrodes that blanket the imaging domain. The simultaneous activation of multiple electrodes can be advantageous to manipulate the resulting field distributions and capture additional spatial information for imaging purposes. However, conventional methods for sensitivity map computation in ECT are not adequate in the presence of multielectrode activation because the mutual coupling between electrodes is not properly accounted for. This coupling becomes especially critical in adaptive electrical capacitance volume tomography (AECVT) sensors, where signals from many small electrode segments are combined into synthetic electrodes. A more general approach is presented for sensitivity map computation in AECVT based on the actual posteriori (induced) charge distributions rather than the conventional approach based on boundary conditions with no account for electrode interactions.
Electrical Capacitance Volume Tomography (ECVT) has shown to be an effective low-cost and high-speed imaging technique suitable for many applications, including 3D reconstruction of multiphase flow systems. In this paper, we introduce the concept of adaptive ECVT based upon the combination of a large number of small individual sensor segments to comprise synthetic capacitance “plates” of different (and possibly noncontiguous) shapes while still satisfying a minimum plate area criterion set by a given SNR. The response from different segments is combined electronically in a reconfigurable fashion. The proposed adaptive concept paves the way for ECVT to be applicable in scenarios requiring higher resolution and dynamic imaging reconstruction.
A dynamic volume imaging based on the principle of electrical capacitance tomography (ECT), namely, electrical ca-pacitance volume tomography (ECVT), has been developed in this study. The technique generates, from the measured capacitance, a whole volumetric image of the region enclosed by the geometrically three-dimensional capacitance sensor. This development enables a real-time, 3-D imaging of a moving object or a real-time volume imaging (4-D) to be realized. Moreover, it allows total interroga-tion of the whole volume within the domain (vessel or conduit) of an arbitrary shape or geometry. The development of the ECVT imaging technique primarily encloses the 3-D capacitance sensor design and the volume image reconstruction technique. The electrical ﬁeld variation in three-dimensional space forms a basis for volume imaging through different shapes and conﬁgurations of ECT sensor electrodes. The image reconstruction scheme is established by implementing the neural-network multicriterion optimization image reconstruction (NN-MOIRT), developed ear-lier by the authors for the 2-D ECT. The image reconstruction technique is modiﬁed by introducing into the algorithm a 3-D sen-sitivity matrix to replace the 2-D sensitivity matrix in conventional 2-D ECT, and providing additional network constraints including 3-to-2-D image matching function. The additional constraints fur-ther enhance the accuracy of the image reconstruction algorithm. The technique has been successfully veriﬁed over actual objects in the experimental conditions.
A new noninvasive system for multimodal electrical tomography based on electrical capacitance tomography (ECT) sensor hardware is proposed. Quasistatic electromagnetic ﬁelds are produced by ECT sensors and used for interrogating the sensing domain. The new system is noninvasive and based on capacitance measurements for permittivity and power balance measurements for conductivity (impedance) imaging. A dual sen-sitivity map of perturbations in permittivity and conductivity is constructed. The measured data along with the sensitivity matrix are used for the actual image reconstruction. The new multimodal tomography system has the advantage of using already established reconstruction techniques, and the potential for combination with new reconstruction techniques by taking advantage of combined conductivity/permittivity data. Moreover, it does not require direct contact between the sensor and the region of interest. The system performance has been tested on representative data, producing good results.
A nonlinear image reconstruction technique for ECT using a combined neural network approach
Meas. Sci. Technol. 17 (2006) 2097–2103
A combined multilayer feed-forward neural network (MLFF-NN) and analogue Hopfield network is developed for nonlinear image reconstruction of electrical capacitance tomography (ECT). The (nonlinear) forward problem in ECT is solved using the MLFF-NN trained with a set of capacitance data from measurements based on a back-propagation training algorithm with regularization. The inverse problem is solved using an analogue Hopfield network based on a neural-network multi-criteria optimization image reconstruction technique (HN-MOIRT). The nonlinear image reconstruction based on this combined MLFF-NN + HN-MOIRT approach is tested on measured capacitance data not used in training to reconstruct the permittivity distribution. The performance of the technique is compared against commonly used linear Landweber and semi-linear image reconstruction techniques, showing superiority in terms of both stability and quality of reconstructed images.