Application Of Electrical Capacitance Tomography Research Articles

ECT in a large scale industrial pneumatic conveying system

The application of electrical capacitance tomography (ECT) for monitoring of industrial processes has been studied and proposed by many researchers. Examples can be found in monitoring of multiphase flows or mixing processes in reactors. Demonstrations of the functionality based on lab and test rig measurements have proven the potential of the proposed principles. This paper discusses the application of an ECT system in a heavy industries application. The harsh operating conditions in the industrial environment pose several challenges for the application of sophisticated measurement technology. This work addresses key aspects for the application of an ECT measurement system in such an environment. Specifically the electrical system design and the influence of the temperature are addressed. Relevant parameters and possible solutions are discussed. Furthermore the application of ECT as instrument for mass flow metering in pneumatic conveying processes is addressed. Supportive measurement studies from test rig experiments and comparative simulation studies are presented. Therefore, the paper provides a concise discussion on the application of ECT under harsh operating conditions, as well as the use of ECT as a measurement device for process measurement. The work concludes with a presentation of a measurement system in an industrial plant in which the proposed concepts were succesfully implemented.

DEVELOPMENT OF IMAGE RECONSTRUCTION FOR DETECTING STATIC OIL-GAS REGIMES USING INVASIVE ELECTRICAL CAPACITANCE TOMOGRAPHY IN STEEL PIPE APPLICATION – AN INITIAL STUDY An Initial Study

Electrical Capacitance Tomography (ECT) is a well-known technique for identifying two-phase regimes when a non-conducting medium is the main medium inside a pipe. However, the common non-invasive techniques of ECT are not suitable for monitoring non-conducting systems that use steel pipes. This is because the placement of the common ECT sensor is outside of the pipe and thus cannot penetrate the conducting pipe. Therefore, this paper presents the development of image reconstruction techniques for invasive Electrical Capacitance Tomography (ECT) in steel pipe applications. The study uses eight electrodes for invasive ECT that are independently designed for easy replacement. The shield guard of each electrode is individually designed, unlike the common sensor of ECT. Moreover, it produces a sensitivity map in the forward problem by modeling the geometry to mimic the real hardware of invasive ECT in Comsol Multiphysics livelink with MATLAB. The data from real hardware is exported offline and a linear back projection algorithm is used as the inverse problem. The phantom of gas-oil with different sizes, positions, and multiple phantoms from the range of 20mm, 25 mm and 33mm are tested. The tomograms of the region of interest can be obtained as a result. The paper concludes that the developed image reconstruction techniques for invasive ECT in steel pipe applications can provide acceptable and reliable results. This study can be useful for researchers and practitioners in the field of ECT and steel pipe applications.

CGAN-ECT: Reconstruction of Electrical Capacitance Tomography images from capacitance measurements using Conditional Generative Adversarial Networks

Due to the rapid growth of applications of Electrical Capacitance Tomography (ECT) across diverse industrial sectors, there is a pressing demand for swift and high-fidelity methodologies for image reconstruction from raw capacitance measurements. Deep learning, as an effective non-linear mapping tool for complicated functions, has been going viral in many fields including electrical tomography. This paper introduces a pioneering contribution by proposing a Conditional Generative Adversarial Network (CGAN) model explicitly tailored for the purpose of reconstructing ECT images from capacitance measurements. We propose a novel modulator to transform the raw input capacitance measurements into a 2D image. To the best of our knowledge, this is the first time to represent the capacitance measurements in an image form. We have created a new massive ECT dataset of synthetic image-measurements pairs for training and testing purposes of the proposed model. This extensive ECT dataset comprises 320,000 synthetic image-measurement pairs. It forms the basis for evaluating the feasibility and generalizability of the CGAN-ECT model, with assessments conducted on testing data, contaminated data, and flow patterns which are intentionally excluded from the model’s training phase. The evaluation results prove the efficacy of the proposed CGAN model in generating ECT images with heightened accuracy compared to traditional methods and alternative deep learning-based image reconstruction algorithms. The CGAN-ECT model has succeeded in a achieving an average image correlation coefficient exceeding 99.3%, coupled with an average relative image error of approximately 0.07.

Study and Design of a Multi-range Programmable Sensor for Temperature Measurement

In this paper, a wide-range high-precision sensor has been designed in order to accurately measure the temperature in a medium with arbitrary temperature variation and the implementation of a wide-spectrum temperature measurement system with a self-selected multi-sensor has been realized. This multi-sensor core is made up of different sensors combined to measure different temperature ranges. This concept can be used for high-precision temperature measurement in electrical capacitance tomography applications. The proposed technique is well suited for temperatures in boilers, industries, and everywhere high temperature measurement sensitivity is needed by using different combined temperature sensors of high precision.

Image reconstruction using Z-axis spatio-temporal sampling in 3D electrical capacitance tomography

Three-dimensional (3D) visualization has provoked interest in electrical capacitance tomography (ECT) due to recent progress in the development of capacitance measurement circuits and data-acquisition systems. This paper proposes image reconstruction using spatio-temporal sampling in the Z-axis for 3D ECT to improve spatial image resolution. In a typical application of ECT, multi-phase flow imaging is performed through two-dimensional (2D) data acquisition and 2D reconstruction of image frames over time. In the presented method, a volume of interest (VOI) is reconstructed from several time samples obtained using a 3D sensor. The procedure of Jacobian matrix extension for the spatio-temporal data is described. The experiments were carried out using the EVT4 system and a 32-electrode sensor. The reconstruction results of a VOI from a single 3D measurement and spatio-temporal sampling are compared. A phantom for 3D imaging to assess spatial image resolution using a modulation transfer function is proposed.

A novel computational imaging algorithm based on split Bregman iterative for electrical capacitance tomography

As an advanced detection technology in the industrial field, electrical capacitance tomography (ECT) can better reconstruct the material distribution state in the measured area by selecting the appropriate algorithm. In order to improve the reconstruction quality, this paper devises a novel objective function to model the ECT image reconstruction problem, in which L 1-norm is deployed as data fidelity with the focus on weakening the influence of capacitance outliers on the reconstruction quality, L P regularization reinforces the sparseness of the phantom objects, and L 1 regularization applies to model deviation variables to increase the robustness of the system. Based on the fast-iterative shrinkage thresholding technique and the soft thresholding method as sub-solvers, the split Bregman iterative method is designed as an effective solver for the proposed objective function. Numerical simulation and experimental validate that the proposed algorithm has excellent imaging ability compared with other imaging algorithms, which provides a good choice for the practical application of ECT in the future.

Investigation of Complex Permittivity/Conductivity Distribution by Electrical Tomography

The electrical tomography technology is a multi-phase flow detection technology. This technology has the advantages of non-radiation, non-invasive and visualization, can be widely used in traditional energy field and new energy field. The permittivity and conductivity, as the basic electrical parameters of material, are closely related to the distribution of material in multiphase flow. However, in the actual multiphase flow, due to the non-uniform distribution of permittivity and conductivity in time and space, the accuracy and validity of measuring instruments are challenged. The main target of this research is to investigate the application of electrical capacitance tomography (ECT), electrical resistance tomography with voltage excitation (ERTv) and electrical resistance tomography with current excitation (ERTc) on the multiphase flow with complex permittivity and conductivity distribution. The final objective based on the above research is to provide suitable tomography modual for different multiphase flow process. The experimental results show that ECT is suitable for measuring dry gas-solid fluids. ERTv is better for measuring high water content gas-solid fluids and low conductivity gas-liquid fluids, while ERTc is better for measuring continuous phase conductivity gas-liquid fluids.

Simultaneous Shape and Permittivity Reconstruction in ECT With Sparse Representation: Two-Phase Distribution Imaging

Electrical capacitance tomography (ECT) is a technique to visualize the permittivity distribution inside a domain from the interelectrode mutual capacitances on the domain boundary. The potential applications of ECT in nondestructive testing (NDT) depend on accurate and stable image reconstruction. To improve image quality, this article presents a reconstruction framework with the sparse representation of phase boundaries in two-phase distributions. By adopting a sparsity-promoting basis, i.e., radial basis functions, the reconstruction problem is transformed into searching for the optimal phase boundaries and real-permittivity values of inclusions by formulating an optimization problem accordingly. This can reduce the number of unknowns significantly and has a strong ability to accommodate to the topology changes of inclusion geometries, which can improve the reconstruction accuracy and robustness. Both simulation and phantom experiments are performed to investigate the reconstruction performance, especially regarding the antinoise ability and robustness against choices of model parameters. Compared with typical conventional methods, our method can not only provide significantly improved image quality but also accurately estimate the real-permittivity values of inclusions. This would enable ECT to be applied in challenging NDT applications.

Permittivity Reconstruction in Electrical Capacitance Tomography Based on Visual Representation of Deep Neural Network

Electrical Capacitance Tomography (ECT) has been developed for many years and made great progresses. Successful applications of ECT depend on the accuracy and speed of image reconstruction. In this paper, we propose a new image reconstruction method based on deep neural network. The proposed neural network mainly consists of three parts, i.e. a forward problem network, an inverse problem network and a permittivity prediction network. Compared to previous reconstruction algorithms, the benefit of our method is that it can not only reconstruct the object shape, but also predict its permittivity value with the preset resolution which is desired in many multiphase flow applications. In experiment, 10000 frames of simulation data and additional measured capacitance data were used. The results show that the proposed method can reconstruct images more accurately than typical iterative methods with real permittivity values of objects predicted correctly, and reduce the computational cost to about 2.24 seconds per frame for an image resolution of 200*200.

A Novel Image Reconstruction Strategy for ECT: Combining Two Algorithms With a Graph Cut Method

Image reconstruction plays a key role in the application of electrical capacitance tomography (ECT). Although many different algorithms have been developed in the past, it is often difficult to obtain satisfactory images in all imaging regions by the use of a single algorithm due to the soft-field nature of ECT. This motivated us to develop a novel ECT image reconstruction strategy, in which a high-quality image can be obtained by combining the images reconstructed by two different algorithms via a graph cut method. By doing so, it is possible to retain the advantage of each algorithm for specified imaging regions and improve the quality of the whole image. This strategy was verified both numerically and experimentally for two widely used ECT reconstruction algorithms, i.e., linear backprojection and Tikhonov regularization. The results for stationary objects as well as gas–solid fluidized beds demonstrated that this graph-cut-based combination strategy presents a promising approach for ECT image reconstruction.

Application of Electrical Capacitance Tomography for Imaging Conductive Materials in Industrial Processes

This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior.

Effect of Electrode Length of an Electrical Capacitance Tomography Sensor on Gas−Solid Fluidized Bed Measurements

Electrical capacitance tomography (ECT) has been widely used in fluidized bed measurements because of the noninvasive and nonintrusive features. The successful application of ECT in fluidized bed measurements depends on the appropriate design of sensors. This paper discusses the effect of ECT sensor length on fluidized bed measurements by simulations and experiments. The sensitivity distributions in different axial positions of a sensor are compared, which shows the similarity of sensitivity distribution contours and decline of intensity along the sensor axial direction. Fluidized beds of three different diameters (45, 55, and 65 mm) are used to compare the effect of sensor electrode length on fluidized bed measurements. In each fluidized bed, five sensors with different electrode lengths are tested. The experimental results show that the sensor electrode length has significant effect on bubble detection in fluidized bed measurements, and it is recommended that the sensor electrode length should be as short as possible considering the signal-to-noise ratio constraint.

Regularization parameter optimization based on the constraint of Landweber algorithm for electrical capacitance tomography

Image reconstruction algorithms play an important role in practical applications of electrical capacitance tomography. In the present paper, a combined image reconstruction method is proposed, which takes the results of Landweber algorithm as the constraint condition of Tikhonov algorithm's regularization parameter, calculates the regular parameter, inverts the inverse matrix of sensitivity matrix, and finally obtains the dielectric constant distribution; thus, reconstructed images with improved clarity were obtained. Simulation test are carried out to evaluate and analyze the proposed method from image error, correlation coefficient, image reconstruction time, and anti-noise ability. The results revealed that the Tikhonov regularization algorithm had excellent anti-noise ability; thus, it significantly improved the clarity of reconstructed images and clearly distinguished the multi-phase flow pattern and distribution.

High-temperature electrical capacitance tomography for gas–solid fluidised beds

Electrical capacitance tomography (ECT) is a non-intrusive and non-invasive imaging technique for the visualisation of material distribution, e.g. in a gas–solid fluidised bed. So far, ECT has been successfully used in gas–solid fluidised beds at ambient temperature. However, ECT has been rarely used in high-temperature gas–solid fluidised beds, which is of practical importance in many applications. Considering that most fluidised bed reactors are operated at high temperature, it is necessary to investigate the application of ECT in high-temperature industrial processes. In this work, a high-temperature ECT sensor, which can withstand 1073 K, is designed and fabricated. It has been verified by three stationary objects. The results show that the ECT sensor can give satisfactory images at different high-temperature levels and the change in temperature has little effect on the signal-to-noise ratio. It has also been found that the minimum bubbling velocity estimated by ECT is the same as that obtained by pressure drop measurements, and two fluidisation regimes, i.e. bubbling and slugging can be identified by ECT.

Reconstruction method of electrical capacitance tomography based on wavelet fusion

The accuracy of reconstructed images of electrical capacitance tomography (ECT) is a bottleneck concerning the successful application of ECT. An image data fusion algorithm based on wavelet transform was proposed in this paper to improve the accuracy of reconstructed images. First, reconstructed images were obtained using conjugate gradient least square algorithm and Landweber iterative algorithm, respectively. Secondly, reconstructed images were decomposed by wavelet transform. After that, the approximate component was processed according to the weighted average fusion rule. The detail component was processed according to the maximum fusion rule of absolute value. Finally, the new reconstructed images were obtained by wavelet reconstruction. Simulation and static experimental results showed that the reconstructed images with higher accuracy can be obtained after fusion and the artefacts were decreased obviously.

Reconstruction Method of Electrical Capacitance Tomography Based on Wavelet Fusion

The accuracy of reconstructed images of Electrical Capacitance Tomography (ECT) is a bottleneck concerning the successful application of ECT. An image data fusion algorithm based on wavelet transform was proposed in this paper to improve the accuracy of reconstructed images. First, reconstructed images were obtained using conjugate gradient least square algorithm and Landweber iterative algorithm, respectively. Secondly, reconstructed images were decomposed by wavelet. After that, the approximate component was processed according to the weighted average fusion rule. The detail component was processed according to the maximum fusion rule of absolute value. Finally, the new reconstructed images were obtained by wavelet reconstruction. Simulation and static experimental results showed that the reconstructed images with higher accuracy can be obtained after fusion and the artefacts were decreased obviously.

Pneumatic Flow Characterization Using ECT Tomography Images Analysis

Abstract The control of dense phase pneumatic convening process is not a trivial task. The control of bulk solids flow phenomena requires detailed knowledge about the temporal and spatial changes of material concentration during flow. This paper describes application of electrical capacitance tomography for the characterization of the pneumatic conveying of solids. The level of concentration changes obtained with the aid of tomography imaging and calculated flow velocity allows monitoring flow behaviour during the granular material transport process. The cross-correlation technique applied for image processing as well as the calculated pixels-based flow velocity profile together with the proposed visualisation method of tomography images sequence provide information about flow conditions. The study of flow characterization, based on ECT data, was conducted for measurements gathered with different flow conditions, including flow blockage.

Investigation of gas–solid bubbling fluidized beds using ECT with a modified Tikhonov regularization technique

Electrical capacitance tomography (ECT) provides a non‐intrusive means to visualize cross‐sectional material distribution of gas–solid bubbling fluidized beds. Successful application of ECT strongly depends on the image reconstruction algorithm used. For on‐line measurements of bubbling fluidized beds, employing an algorithm that can produce high‐quality images without extensive computation is necessary. Using the conventional Tikhonov regularization algorithm, image quality in the central area is basically satisfied but suffers from artifacts in the near‐wall region. To solve this problem, a similar division operation learned from linear back projection was introduced to modify the conventional Tikhonov algorithm. Both numerical simulations and experiments were performed to evaluate the modified technique. The results indicate that the artifacts can be effectively removed and the reconstructed image quality is similar to Landweber method with dozens of iterations. Furthermore, the modified Tikhonov technique shows high accuracy when obtaining important hydrodynamic parameters in gas–solid bubbling fluidized beds. © 2017 American Institute of Chemical Engineers AIChE J, 63: 29–41, 2018

A Pipelined Parallel Hardware Architecture for 2-D Real-Time Electrical Capacitance Tomography Imaging Using Interframe Correlation

This paper presents a new hardware algorithm for real-time and nonlinear 2-D electrical capacitance tomography (ECT) imaging, along with its parallel hardware architecture. A potential application of this system is to reconstruct in real time cross-sectional image of a two-phase fluid with different dielectric constants when it passes through a given section of a pipeline. The proposed hardware algorithm explores the spatial correlation that may occur between one or several consecutive frames. It uses this property to reformulate the classical regularized forward and inverse problems that are indeed very time consuming, preventing them to be used for fast ECT applications. As a result, the proposed algorithm features one-single-step iteration with a substantial reduction of the size of the Jacobian matrix. In addition, the intrinsically parallel feature of the algorithm makes it suitable for parallel hardware architecture. This architecture is based on a pipeline multiprocessor architecture using advanced features of field-programmable gate array technology. It explores the variable bit-width and floating-point multipliers array available in the digital signal processor blocks, to cooperatively perform the partial matrix product with associated arithmetic and logic units, and distributed memory. The experimental results obtained on a two-phase flow loop demonstrate the capability of the system to build in real time and with good accuracy (e.g., less than 3% error) the cross-sectional image of the fluid passing through the pipeline. Around 560 frames of 4096 moving boundary pixels can be reconstructed in 1 s using IEEE754 floating-point data representation and a clock frequency of 400 MHz, for a total power consumption of less than 33 W.

Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure

Abstract A new approach to solve the inverse problem in electrical capacitance tomography is presented. The proposed method is based on an artificial neural network to estimate three different parameters of a circular object present inside a pipeline, i.e. radius and 2D position coordinates. This information allows the estimation of the distribution of material inside a pipe and determination of the characteristic parameters of a range of flows, which are characterised by a circular objects emerging within a cross section such as funnel flow in a silo gravitational discharging process. The main advantages of the proposed approach are explicitly: the desired characteristic flow parameters are estimated directly from the measured capacitances and rapidity, which in turn is crucial for online flow monitoring. In a classic approach in order to obtain these parameters in the first step the image is reconstructed and then the parameters are estimated with the use of image processing methods. The obtained results showed significant reduction of computations time in comparison to the iterative LBP or Levenberg-Marquard algorithms.