Rafiul K. Rasel

Electrical Capacitance Volume Tomography (ECVT) has emerged as an attractive technology for addressing instrumentation requirements in various energy-related multiphase flow systems. ECVT can monitor multiple flow conditions and reconstruct real-time 3D images from capacitance measurements using a large set of electrode plates placed around the processes column enclosing the sensed flow system. ECVT is non-intrusive and allows the measurement of changes in mutual capacitance between all possible plate pair combinations. The objective of this paper is to provide a comprehensive review of recent advances in ECVT, enabling robust monitoring of multiphase flows, especially water-containing multiphase flows.

Measurement of phase volume fractions in water-containing multiphase flows is necessary for the optimization of a host of industrial flow processes. Many water-containing multiphase flows can be classified as either water-dispersed or water-continuous mixtures. A recently developed approach based on Hanai’s mixture formula and utilizing electrical capacitance tomography (ECT) sensors have shown good potential for obtaining water volume fraction estimates in two-phase water-containing flows with different water salinity levels. However, the proposed approach was investigated via controlled experiments restricted to static configurations while, in practice, multiphase flows can be dynamic and unpredictable. In this work, we perform a flow loop study of the proposed ECT-based method for volume fraction estimation in oil–water two-phase flows. We evaluate the performance of the proposed method in both water-dispersed and water-continuous flow regimes by employing different types of capacitive sensors in cylindrical arrangements and parallel-plate rectangular arrangements.

The estimation of water volume fraction in water-containing multiphase flows is important for several industrial applications. There are many published works detailing with methods to estimate and monitor water-containing multiphase flows based on different sensor modalities, including electrical capacitance tomography (ECT). A recently developed method based on ECT sensors to estimate the water volume fraction in water-containing two-phase flows utilizes the Hanai’s mixture formula. Fresh or tap water is conductive and has large electrical permittivity, thus making the ECT-based estimation problem highly nonlinear and challenging to solve. Most of these works assume that the mixture state of water in the two-phase flow is known as either continuous or disperse. However, in practice, the state of water might not be known a priori. In this work, we propose a deep learning based approach to classify water-containing flows into water-dispersed or water-continuous flows and to estimate the water volume fraction present in the flow.

Real-time monitoring of water volume fraction in multiphase flows is an important problem for a number of industrial applications. The water phase in the multiphase flows may correspond to either the dispersed phase or the continuous phase. In the past, several low-cost and nonintrusive techniques based on the electrical capacitance tomography (ECT) has been developed to image and monitor in real-time multiphase flows containing water. However, such monitoring becomes increasingly challenging for high salinity levels, and no reliable ECT-based method is presently available which could work for obtaining water volume fraction in multiphase flows for all water salinity levels. In this paper, we propose a new approach based on the Hanai’s formula for complex dielectric constant and taking advantage of the Maxwell-Wagner-Sillars effect to obtain, to a good approximation, water volume fractions in multiphase flows containing water as either dispersed or continuous phase.

Electrical capacitance tomography (ECT) is a widely used imaging modality to image two-dimensional cross sections of multiphase flows. Recent developments in electrical capacitance volume tomography (ECVT) have made it possible to directly obtain volumetric images from measured data. ECVT is instrumental for obtaining accurate phase hold up information and velocity information that are needed for optimization of certain flow processes. However, compared to ECT, the high correlation between measurements in ECVT exacerbates the ill-conditioning of the associated image reconstruction problem. Previous studies have suggested that neglecting mutual capacitance data between ECVT electrodes located at cross-planes that are well separated along the sensor axis can be done without significantly affecting the reconstructed image. In addition, this may help constrain the ill-conditioning of the reconstruction problem. In this paper, we examine in detail and quantify the effect of reduced cross-plane acquisition strategies for optimizing image reconstruction and constraining the ill-conditioning of typical ECVT settings.

Imaging of multiphase flows holding water as continuous phase (i.e. water dominated flows) is very challenging for conventional electrical capacitance tomography (ECT) due to the high permittivity of water. In this work, we introduce a new approach, based on the multi-frequency excitation of ECT sensors, for imaging and real-time monitoring of waterdominated columnar or slug vertical flows. The proposed method exploits differences between measurements obtained at distinct frequencies caused by the Maxwell-Wagner-Sillars effect, which is present in multiphase flows with at least one conducting phase. To illustrate this new approach, several numerical simulations are carried out for two-phase and three-phase mixtures containing air, methylamine, and/or oil as dispersed phases and with water as the continuous phase. Experimental results are also provided to validate the findings.

We report on recent progress in the development of capacitive sensors for real-time tomography of industrial processes. In particular, we describe recent developments on: (1) adaptive electrical capacitance volume tomography (AECVT) sensors based on reconfigurable synthetic electrodes, (2) multifrequency acquisition strategies exploiting the Maxwell-Wagner-Sillars effect in multiphase flows, (3) displacement current phase tomography for imaging of fluid flows, and (4) flow velocity profiling based on electrical capacitance volume tomography (ECVT) sensors.

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.