Electrical Capacitance Tomography Data Research Articles

Noninvasive Image Reconstruction of Flowing Thrombus With Electrical Capacitance Tomography

A method for noninvasive real-time measurement of blood cardiopulmonary bypass is proposed by electrical capacitance tomography (ECT). An eight-electrode sensor ECT data acquisition system is designed, including sensor design and data acquisition module design. The system has a real-time imaging speed of 40 frames per second (FPS) and an average signal-to-noise ratio (SNR) of 64.59 dB per channel, which has been experimentally verified. First, the system is able to detect 6.25%–42.25% of the target area ratio (AR) when the sensor inner diameter is 20 mm, the outer diameter is 25 mm, and the image correlation coefficient (ICC) is not less than 0.7, which obtained by static experiments. Second, the corresponding voltage sum curve is different when different sizes of targets flow through the electrode coverage area. Dynamic experiments have verified that the sum of voltage curve for each frame image is available to further determine the size of the thrombus generated during the circulation process. Finally, dynamic experiments were performed using fresh porcine blood clots to mimic thrombus, which verified the feasibility of using ECT for in vitro thrombus detection.

Measurement of mixing time in a gas-liquid mixing system stirred by top-blown air using ECT and image analysis

The non-invasive acquisition of mixing state quality of the gas-liquid two-phase flow stirred by top-blown air (GTFSTA) is one of the difficulties in studying Isa reactor used for copper smelting. With the help of electrical capacitance tomography (ECT)-based visualization experiments, in this work, a powerful and effectively hybrid strategy is proposed to measure the mixing state quality of the GTFSTA. Specifically, the GTFSTA with thermal condition is successfully visualized by an ECT-based measurement system. After chaos detection by the three-state test (3ST) method, the mixing time of GTFSTA system under hot state is accurately estimated by Logistic regression method for the first time. Experimental and calculated results show that ECT can be used to visualize the flow pattern of which accuracy reaches 98.69%. The case or working condition (Q =2.0 m3/h, H = 30 cm and T = 45 °C) reaches the mixing state with the mixing time 6.5 s, and the best state of the gas-liquid mixing system is chaotic. The new method can well characterize the flow state in the GTFSTA system and can provide theoretical basis for the actual production process. It not only enriches the analysis method of ECT measurement data, but also creates conditions for the engineering application of online detection system based on ECT data.

Adaptive filtering techniques for velocity estimation of tomographic electric capacitance signals of two-phase gas/oil flows

The development of adaptive real-time flow velocity estimation algorithms for two-phase flows can contribute to monitoring the pipelines of various complex processes, such as energy, chemical, petroleum and nuclear industries. Among the different non-invasive tomography techniques, electrical capacitance tomography (ECT) is gaining increasing attention for its potential use in real-time imaging and characterization of multiphase flow systems. The nature of ECT signals for two-phase flows can significantly degrade the velocity estimation process with cross-correlation approaches. We address the unique challenges of such signals and propose a preprocessing technique to improve the performance and robustness of the velocity estimation algorithm. Two adaptive filters are used to estimate the velocity of a two-phase type flow. A least mean square (LMS) and a fast block LMS (FBLMS) are used to model the time delay between the two signals captured by the twin sensor (ECT). Performance of the proposed technique is assessed by applying it to ECT data obtained from an experimental flow rig. The computed estimates are then compared with the calculated velocity from tracking motion of bubbles captured by a high speed camera monitoring the two phase flow in the pipe. Results show that the proposed technique provides consistent results across various flow patterns, and is advantageous compared to cross-correlation based techniques, specially for chaotic flow conditions. Furthermore, the proposed estimation algorithms can be applied to other electric based tomographic techniques.

Electrical Capacitance Tomography Sensor Using Internal Electrodes

The electrical capacitance tomography (ECT) technique has been extensively studied for real-time tomographic imaging of various industrial processes. Due to the ‘soft-field’ and under-determined problems in the image reconstruction, the tomographic images of ECT are usually low in the spatial resolution when compared with its radioactive counterparts, i.e. $\gamma $ -ray tomography and computed tomography. This paper presents an ECT sensor that consists of 8 external electrodes and 8 internal electrodes, with enhanced image quality at the center of imaging region and better accuracy on phase fraction measurement. The internal electrodes are placed on cross planes inside the investigated vessel. In this way, the signal-to-noise ratio of measurement channels as well as image quality are improved. Numerical simulations show that the proposed sensor can achieve better reconstructed images than a conventional 12-electrode sensor. In addition, the phase fraction calculation using ECT data can also benefit from the internal electrodes and corresponding measurements, which is an important parameter for multiphase flow processes.

PROCESS TOMOGRAPHY FOR MODEL FREE ADAPTIVE CONTROL (MFAC) VIA FLOW REGIME IDENTIFICATION IN MULTIPHASE FLOWS

Multiphase flows are frequently found with oil/gas/water/sand in the oil & gas industries, in processes handling dry particulates such as fluidized bed and particulate flow and slurries and sedimentation, to quote a few industrial applications. All these processes have different flow regimes with characteristic distribution of the different materials in the flow. With increasing sensor data from processes and associated possibilities for data fusion, process tomography offers non-intrusive real time sensing methods for identifying these flow regimes, which in certain cases can lead to hazards to personnel and plants in the process industries. In this paper, two scenarios of control using process tomography are presented from the oil and gas industries and powder technology. Twin plane Electrical Capacitance Tomography (ECT) with a plethora of other sensors for measuring pressure, flow rate in two phase flow involving water and air is one application. The other is particulate flow in a fluidized bed. In the case of water and air, ECT was used in a multiphase flow loop with different combinations of air and water mass flow rates enabling the generation of different flow regimes. In the case of particulate flow, different scenarios of flow conditions were generated using particulates and observing the flow regimes based on the distribution and flow rate of the particulates. For the identification of flow regimes, capacitance values from the 12-electrode ECT module at a rate of 100 frames in 200ms, were logged in for data analytics. In the final stages of using the processed data, in the case of two-phase flow 5 outputs consisting of the identified flow regimes, viz. plug, slug, annular, stratified and wavy flows were used as inputs for control and decision making. Similarly, in particulate flow ECT data were used to estimate the air velocity for fluidization as well as in identifying slug and plug flow, which are valuable to the process engineer in determining the range of air velocities for safe operation of the system used for particulate transport, often involving fluidization bed and pipelines. Tomography is used to discern features and tomometry is used in implementing the algorithms.

Enhancing Resolution of Electrical Capacitive Sensors for Multiphase Flows by Fine-Stepped Electronic Scanning of Synthetic Electrodes

Electrical capacitance tomography (ECT) exhibits several attractive features that are important for industrial process tomography applications. These features include low cost, high speed, and nonintrusive nature. However, due to its soft-field character, a relatively low image resolution is an outstanding challenge for ECT. While many efforts have been made to tackle this challenge by improvements in image reconstruction algorithms, relatively less has been done to enhance the basic ECT hardware sensor configuration and data acquisition process. In this paper, a new measurement acquisition strategy is introduced to increase image resolution when using adaptive ECT (AECT). The proposed strategy is based on the manipulation of synthetic electrodes plates (“metaplates”) formed by a set of combined smaller physical electrodes (segments). The synthetic electrodes are sequentially activated with partial overlap of constituent segments to provide a fine-stepped axial and/or azimuthal electronic scan along the entire sensor. Consequently, an increased number of independent capacitance measurements are made available. Reconstruction results using the proposed measurement acquisition strategy (AECT) are shown to illustrate the enhanced resolution and stability in the imaging of objects compared to conventional ECT sensors.

Electrical Capacitance-Based Flow Regimes Identification—Multiphase Experiments and Sensor Modeling

Electrical capacitance tomography (ECT) has been used in flow regime identification studies. Usage of raw measurements for direct time series analysis of the raw ECT data removes the need for image processing, leading to easier and faster recognition of flow regimes with non-invasive ECT sensor arrays. Based on raw ECT data, a simple algorithm that can be implemented online and provide a priori knowledge of the flow regimes is presented in this paper. In the approach proposed by us, the time series of each inter-electrode normalized capacitance measurements are taken over a suitable duration. Four mathematical operations are considered for data reduction: averaging, computation of standard deviation (SD), and high-pass and low-pass filtering. Eigenvalues of the matrices have been used for recognition of spatial features, free of artefacts caused by rotations. At this stage, the methodology categorizing flowing into stratified smooth, stratified wavy, annular, slug, or plug regimes has been tested with a significant data set of measurements of fully developed air-water flows in horizontal 56-mm diameter pipe. For selected flow regimes, four criteria, based on eigenvalues of matrices formed by raw data from the array of sensors, are tested and validated using both experimental and adequate numerical models of the sensor arrays. Based on the results presented in this paper, the approach using “frame-by-frame” time series analysis is capable of giving valuable process insight using non-intrusive sensing techniques and can be easily adapted to other sensing modalities.

Imaging of oil-water flow patterns by Electrical Capacitance Tomography

Electrical Capacitance Tomography (ECT) is well established as a tool for multiphase flow studies when permittivities of the media in the flow, are not too different, e.g. for oil and gas. In this work, we investigate how ECT performs for high permittivity contrasts as for oil and water. The main objective of the study has been to perform analysis on ECT data to identify flow regimes. Different oil-water flow patterns were generated for a range of mixture velocities (0.25–1.5m/s) and water cuts (0–97.5%) at different pipe inclinations (−5 °,−1°, 0°, +1°, +5°). The test fluids were deionized water and mineral oil (Exxsol D60). The tests were performed in a 15m long, inclinable stainless steel pipe with inner diameter of 56.3mm. Flow patterns were determined by visual observation and high-speed video. Cross-sectional images of flow patterns were obtained using ECT to study how far so called “soft-field” sensing methods can reveal details of multiphase flow. Normalized data of individual inter-electrode capacitances were analyzed to obtain local volume fractions of the two phases for each representative flow pattern. Gamma densitometry was deployed along with ECT for obtaining simultaneous measurements and comparing with volume fractions estimated using inter-electrode capacitance values.Even though ECT is a fast data acquiring and online flow imaging technique, the image resolution is low or biased due to several factors. Some of the limitations are due to the maximum number of independent capacitance measurements possible with the ECT module used. There are some shortcomings of the Linear Back Projection (LBP) image reconstruction algorithm, also higher permittivity contrast between oil and water contributes for deficiencies of the ECT images and there is dependence on accurate sensitivity mapping for the various electrode combinations. Off-line iterative image reconstruction improved the image quality and the accuracy of the estimation of volume fractions. ECT images were sufficient for qualitative identification of flow patterns, but it was difficult to acquire image details of the flow such as droplets and small-scale phase distributions.The local volume fractions revealed by inter-electrode capacitance measurements and gamma densitometry were in reasonable agreement. These same capacitances showed consistency with negligible standard deviation, while the standard deviation of the gamma densitometer readings were much higher. However, it is possible to determine flow patterns based on a combination of inter-electrode capacitance measurements, together with volume fraction variations.

Imaging metallic samples using electrical capacitance tomography: forward modelling and reconstruction algorithms

Electrical capacitance tomography (ECT) is an imaging technology used to reconstruct the permittivity distribution within the sensing region. So far, ECT has been primarily used to image non-conductive media only, since if the conductivity of the imaged object is high, the capacitance measuring circuit will be almost shortened by the conductivity path and a clear image cannot be produced using the standard image reconstruction approaches. This paper tackles the problem of imaging metallic samples using conventional ECT systems by investigating the two main aspects of image reconstruction algorithms, namely the forward problem and the inverse problem. For the forward problem, two different methods to model the region of high conductivity in ECT is presented. On the other hand, for the inverse problem, three different algorithms to reconstruct the high contrast images are examined. The first two methods are the linear single step Tikhonov method and the iterative total variation regularization method, and use two sets of ECT data to reconstruct the image in time difference mode. The third method, namely the level set method, uses absolute ECT measurements and was developed using a metallic forward model. The results indicate that the applications of conventional ECT systems can be extended to metal samples using the suggested algorithms and forward model, especially using a level set algorithm to find the boundary of the metal.

Measurement of bubble sizes in fluidised beds using electrical capacitance tomography

Electrical capacitance tomography (ECT) provides a means for non-invasively imaging multiphase flows, such as those in fluidised beds. Traditionally ECT images are reconstructed using the assumption that the distribution of permittivity varies smoothly throughout the sensor region. However, for many applications there are step changes in the permittivity, for example, between the bubble and particulate phases in a fluidised bed, and the assumption of smoothness is flawed. In this article a Total Variation Iterative Soft Thresholding (TV-IST) algorithm is used to reconstruct ECT images that allows for sharp transitions in the permittivity distribution. This new algorithm has been compared with established algorithms for ECT image reconstruction. It was found that the TV-IST algorithm reduced the sensitivity to the threshold level chosen when extracting measurements of bubble size from ECT data sets. Measurements of the bubble size distribution in the fluidised bed using the TV-IST algorithm agreed closely with established empirical correlations for the size of bubbles. The results demonstrate that ECT can provide accurate and high spatial resolution measurements of features such as bubbles in gas-solid fluidised beds.

Applications of Electrical Capacitance Tomography for Research on Phenomena Occurring in the Fluidised Bed Reactors

Abstract The paper presents a review of current achievements in the Electrical Capacitance Tomography (ECT) in relation to its possible applications in the study of phenomena occurring in fluidised bed reactors. Reactors of that kind are being increasingly used in chemical engineering, energetics (fluidised bed boilers) or industrial dryers. However, not all phenomena in the fluidised bed have been thoroughly understood. This results in the need to explore and develop new research methods. Various aspects of ECT operation and data processing are described with their applicability in scientific research. The idea for investigation of temperature distribution in the fluidised bed, using multimodal tomography, is also introduced. Metrological requirements of process tomography such as sensitivity, resolution, and speed of data acquiring are noted.

Fast and robust 3D electrical capacitance tomography

Electrical capacitance tomography (ECT) is well established for 2D imaging of multiphase flow. Increasingly, ECT is now being used to image in 3D. In this paper we examine the challenges of 3D image reconstruction using simulations of a 24 electrode (4 planes of 6 electrodes each) sensor. In particular, we demonstrate that the changes in capacitance in a 3D sensor can be as much as an order of magnitude less than in a 2D sensor and that the condition number for 3D imaging can be of the order of 106. We show that the condition number for 3D imaging with this sensor is dominated by the contributions from the most widely separated electrodes. If these pairs of electrodes are eliminated the condition number can be reduced by up to four orders of magnitude. Interestingly, although cross-plane measurements are essential for accurate reconstruction of 3D images, measurements from the widely separated electrodes are shown to have little influence on the quality of images that can be reconstructed, even in the noise-free case. This finding leads us to propose a new sampling strategy for 3D ECT in which only those capacitance measurements from nearby electrodes are included. This sampling approach leads to a reduction in acquisition time for each ECT data set by 40%, with no degradation in image quality and increased robustness to noise. We demonstrate our findings using experimental measurements on a 3D sensor.

An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data

This paper presents novel research on the development of a generic intelligent oil fraction sensor based on Electrical capacitance Tomography (ECT) data. An artificial Neural Network (ANN) has been employed as the intelligent system to sense and estimate oil fractions from the cross-sections of two-component flows comprising oil and gas in a pipeline. Previous works only focused on estimating the oil fraction in the pipeline based on fixed ECT sensor parameters. With fixed ECT design sensors, an oil fraction neural sensor can be trained to deal with ECT data based on the particular sensor parameters, hence the neural sensor is not generic. This work focuses on development of a generic neural oil fraction sensor based on training a Multi-Layer Perceptron (MLP) ANN with various ECT sensor parameters. On average, the proposed oil fraction neural sensor has shown to be able to give a mean absolute error of 3.05% for various ECT sensor sizes.

Real-Time Reconstruction of Moving Objects in an Electrical Capacitance Tomography System Using Inter-Frame Correlation

In this paper, two new electrical capacitance tomography (ECT) algorithms for real-time reconstruction of a sequence of images of moving objects passing through a given two-dimensional section of electrical electrodes are presented. They both explore the inter-frame correlation that usually exists between consecutive frames and use this information as an additional constraint during the reconstruction of the actual frame in solving both its forward and inverse problems. In addition, contrary to the Kalman filter-based tomography technique, both suggested methods maintain the intrinsic non-linearity property of the inverse and forward problems. The proposed algorithms were tested using both synthetic and real ECT data for some moving objects and by adding up to 7% of noise in the values of capacitances. The corresponding experimental results demonstrate that the use of inter-frame correlation for ECT image reconstruction can lead to a significant improvement in image quality, execution time, and stability. Hence, a processing time of up to 175 frames/s was achieved on a single processor PC compatible computer. This interesting performance would be even higher if a dedicated parallel hardware architecture implements some intrinsically parallelizable, though time consuming, subroutines required by the proposed methods, such as matrix multiplication and matrix inversion, is used.

Capacitance–Based Tomography Flow Pattern Classification Using Intelligent Classifiers With Voting Technique

This paper presents a method for Electrical Capacitance Tomography (ECT) flow classification using voting technique, employing Multilayer Perceptrons (MLPs) as the intelligent pattern classifiers. MLP classifiers were trained with a set of simulated ECT data associated to various flow patterns and was tested with untrained data to verify their performances. MLP classifiers which gave high percentage of correct classification were integrated into a voting system and tested over a distinct set of ECT data. The performances of the individually selected classifiers were compared with the voting system. The results showed superiority of the voting system over individual classifiers. Key words: Electrical capacitance tomography; multilayer perceptron; voting; pattern classification; ensemble neural network

Crack detection in dielectric objects using electrical capacitance tomography imaging

Dielectric materials are becoming widely used because of their versatility and low cost. Examples include carbon fibre, glass reinforced plastic, polymers and ceramics. These materials are increasingly used for aircraft structures and other important areas, such as transportation in gas pipelines, where materials require regular inspection. There is a need for a new technology for rapid inspection of dielectric materials. This paper focuses on one of the most challenging material inspection problems using an electrical capacitance tomography (ECT) system. The results show that volume cracks can be detected using ECT data and a state-of the-art shape reconstruction algorithm. The reconstruction of cracks is difficult using conventional image-based approaches.

Experimental research of flow patterns and pressure signals in horizontal dense phase pneumatic conveying of pulverized coal

Understanding flow patterns and their variability is important for optimal design and trouble free dense phase pneumatic conveying of pulverized coal in a horizontal tube. Employing the electrical capacitance tomography (ECT), six flow patterns were identified and utilized for quantitative analysis based on the value and distribution of cross-sectional solid concentration. The dense-phase flow patterns in the horizontal tube of the pneumatic conveying system were somehow variable even when the operating conditions were unchanged. The probability calculation results suggest changing multiple flow patterns with one or two dominant flow for each of the seven sets of experimental conveying conditions and that a finite change in the dominant flow pattern would occur with an increasing superficial gas velocity. The power spectral density (PSD) function and the Hurst exponent of the pressure signals of the pulverized coal were well correlated with its flow patterns in a horizontal tube. The PSD functions and probability density functions (PDFs) of the void fraction signals from ECT are found to be related with flow patterns and can be used to quantitatively identify flow regimes. The ECT data may therefore be utilized for monitoring the flow patterns in a horizontal tube employed for pneumatic conveying of pulverized coal.

The influence of quantization process on the performance of global entropic thresholding algorithms using electrical capacitance tomography data

In measuring component fraction in multiphase flows using tomographic techniques, it is desirable to use a high speed tomography system capable of generating 100 tomograms per second. The electrical capacitance tomography system in this regard is considered to be the best among the available tomographic techniques. However, due to its inherent limitations the system generates distorted reconstructed tomograms necessitating the use of extra signal processing techniques such as thresholding to minimize these distortions. Whilst thresholding technique has been effective in minimizing distortions, the additional computation associated with the process limits the speed of tomogram generation desired from the system. Further, the accuracy of the techniques is limited to higher ranges of the full component fraction range. However, since its performance can be influenced by the nature of the quantization process required a priori, optimal quantization parameters can be found and used to improve performance. In this article the influence of quantization resolution and its rate on the performance of global entropic thresholding algorithms have been investigated. Measurement of gas volume component fraction in a multiphase flow of gas/liquid mixture using electrical capacitance tomography system has been used for evaluation using simulated and online capacitance measurement data. Results show that an optimal quantizer resolution is flow regime dependent. Higher resolutions are optimal for annular flow and vice versa for stratified flow regimes. Also, higher resolution significantly minimizes the dependency of the thresholding algorithm on the object to be searched, thereby reducing complexity of designing a thresholder. Overall, the optimal quantization resolution is 256. Tanzania Journal of Science Vol. 31 (2) 2005: pp. 63-76

FOUR-DIMENSIONAL ELECTRICAL CAPACITANCE TOMOGRAPHY IMAGING USING EXPERIMENTAL DATA

Electrical capacitance tomography (ECT) is a relatively mature non-invasive imaging technique that attempts to map dielectric permittivity of materials. ECT has become a promising monitoring technique in industrial process tomography especially in fast flow visualization. One of the most challenging tasks in further development of ECT for real applications are the computational aspects of the ECT imaging. Recently, 3D ECT has gained interest because of its potential to generate volumetric images. Computation time of image reconstruction in 3D ECT makes it more difficult for real time applications. In this paper we present a robust and computationally efficient 4D image reconstruction algorithm applied to real ECT data. The method takes advantage of the temporal correlation between 3D ECT frames to reconstruct movies of dielectric maps. Image reconstruction results are presented for the proposed algorithms for experimental ECT data of a rapidly moving object.

Mass Transfer Coefficient for Drying of Moist Particulate in a Bubbling Fluidized Bed

AbstractExperiments on drying of moist particles by ambient air were carried out to measure the mass transfer coefficient in a bubbling fluidized bed. Fine glass beads of mean diameter 125 μm were used as the bed material. Throughout the drying process, the dynamic material distribution was recorded by electrical capacitance tomography (ECT) and the exit air condition was recorded by a temperature/humidity probe. The ECT data were used to obtain qualitative and quantitative information on the bubble characteristics. The exit air moisture content was used to determine the water content in the bed. The measured overall mass transfer coefficient was in the range of 0.0145–0.021 m/s. A simple model based on the available correlations for bubble‐cloud and cloud‐dense interchange (two‐region model) was used to predict the overall mass transfer coefficient. Comparison between the measured and predicted mass transfer coefficient have shown reasonable agreement. The results were also used to determine the relative importance of the two transfer regions.