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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 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.

Electrical capacitance volume tomography (ECVT) has shown to be an effective low-cost and high-speed imaging technique suitable for many applications, including 3-D 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 capacitance 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 interrogation 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 earlier by the authors for the 2-D ECT. The image reconstruction technique is modified by introducing into the algorithm a 3-D sensitivity 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 further 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 sensitivity 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 new technique for solving the forward problem in electrical capacitance tomography sensor systems is introduced. The new technique is based on training a feed-forward neural network (NN) to predict capacitance data from permittivity distributions. The capacitance data used in training and testing the NN is obtained from preprocessed and filtered experimental measurements. The new technique has shown better results when compared to the commonly used linear forward projection (LFP) while maintaining fast prediction speed. The new technique has also been integrated into a modified iterative linear back projection (Landweber) reconstruction algorithm. Reconstruction results are found to be in favor of the NN forward solver when compared to the widely used Landweber reconstruction technique with LFP forward solver.