Search Results (30)

Accurate multiphase flow measurement in horizontal wells is fundamentally challenged by the antagonistic electrical responses of water and gas: Electrical Resistance Tomography (ERT) loses sensitivity to thin liquid films, while Electrical Capacitance Tomography (ECT) suffers signal saturation in conductive water, preventing either modality from covering the full operating envelope alone. This study proposes a physics-guided hybrid modeling framework that integrates multi-modal ERT/ECT sensing to achieve high-precision flowrate inversion. The framework utilizes a corrected multi-modal fusion algorithm, achieving a liquid holdup MAPE of 2.5 ± 0.5% representing a nearly two-fold improvement over the best single-modality system (Direct ERT, 4.5%). For velocity estimation, an optimized cross-correlation method yields results with ± 3.0% error, incorporating multi-sensor and multi-sequence fusion. A key finding is that deep neural networks exhibit Architectural Phase Specialization: multi-branch architectures (MB-DNN) perform strongly on localized, heterogeneous liquid structures (2.0% liquid error), whereas fully-connected architectures (FC-DNN) excel at capturing the global patterns of the continuous gas core (1.2% gas error). By hybridizing a calibrated drift-flux physical model with these phase-specialized DNNs, the framework achieves overall averaged errors of 1.8% for gas and 1.5% for liquid across the full experimental envelope. The proposed framework was evaluated on 444,313 experimental samples and subsequently validated in a three-month industrial trial at the Puguang gas field under extreme conditions (26 MPa, 80 °C), where it maintained a prediction error of ± 2.3%. This work establishes a scalable, physically consistent paradigm for intelligent hydrocarbon production monitoring. Full article

The Electrical Capacitance Tomography (ECT) imaging pipeline relies on accurate estimation of electric field distributions to compute electrode capacitances and reconstruct permittivity maps. Traditional ECT forward model methods based on the Finite Element Method (FEM) offer high accuracy but are computationally intensive, limiting their use in real-time applications. In this proof-of-concept study, we investigate the use of Graph Convolutional Networks (GCNs) for direct, one-step prediction of electric field distributions associated with a circular ECT sensor numerical model. The network is trained on FEM-simulated data and outputs of full 2D electric field maps for all excitation patterns. To evaluate physical fidelity, we compute capacitance matrices using both GCN-predicted and FEM-based fields. Our results show strong agreement in both direct field prediction and derived quantities, demonstrating the feasibility of replacing traditional solvers with fast, learned approximators. This approach has significant implications for further real-time ECT imaging and control applications. Full article

As the interface between frozen and unfrozen soil, the ice front is not only a spatial location concept, but also a potentially dangerous interface where the mechanical properties of soil could change abruptly. Accurately identifying its spatial position is essential for the safe and efficient execution of large-scale frozen soil engineering projects. Electrical capacitance tomography (ECT) is a promising method for the visualization of frozen soil due to its non-invasive nature, low cast, and rapid response. This paper presents the design and optimization of a mobile circular capacitance sensor (MCCS). The MCCS was used to measure frozen soil samples along the depth direction to obtain moisture distribution and three-dimensional images of the ice front. Finally, the experimental results were compared with the simulation results from COMSOL Multiphysics to analyze the deviations. It was found that the fuzzy optimization design based on multi-criteria orthogonal experiments makes the MCCS meet various performance requirements. The average permittivity distribution was proposed to reflect moisture distribution along the depth direction and showed good correlation. Three-dimensional reconstructed images could provide the precise position of the ice front. The simulation results indicate that the MCCS has a low deviation margin in identifying the position of the ice front. Full article

This study investigated the potential of the generative adversarial neural network (cGAN) image reconstruction in industrial electrical capacitance tomography. The image reconstruction quality was examined using image patterns typical for a two-phase flow. The training dataset was prepared by generating images of random test objects and simulating the corresponding capacitance measurements. Numerical simulations were performed using the ECTsim toolkit for MATLAB. A cylindrical sixteen-electrode ECT sensor was used in the experiments. Real measurements were obtained using the EVT4 data acquisition system. The reconstructed images were evaluated using selected image quality metrics. The results obtained using cGAN are better than those obtained using the Landweber iteration and simplified Levenberg–Marquardt algorithm. The suggested method offers a promising solution for a fast reconstruction algorithm suitable for real-time monitoring and the control of a two-phase flow using ECT. Full article

This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional numerical simulation. The phantom of an axial cross-section of the head without and with impact lesions was an average of a three-centimeter-thick layer corresponding to the height of the sensing electrodes. Stroke was modeled using regions with characteristic electrical parameters for tissues with reduced perfusion. The head phantom included skin, skull bone, white matter, gray matter, and cerebrospinal fluid. The coupling capacitance was taken into account in the 16-electrode capacitive sensor model. A dedicated ECTsim toolkit for Matlab was used to solve the forward problem and simulate measurements. A conditional generative adversarial network (cGAN) was trained using a numerically generated dataset containing samples corresponding to healthy patients and patients affected by either hemorrhagic or ischemic stroke. The validation showed that the quality of images obtained using supervised learning and cGAN was promising. It is possible to visually distinguish when the image corresponds to the patient affected by stroke, and changes caused by hemorrhagic stroke are the most visible. The continuation of work towards image reconstruction for measurements of physical phantoms is justified. Full article

Propellant mass gauging under micro-gravity conditions is a challenging task due to the unpredictable position and shape of the fuel body inside the tank. Micro-gravity conditions are common for orbiting satellites and rockets that operate on limited fuel supplies. Capacitance sensors have been investigated for this task in recent years; however, the effect of various positions and shapes of the fuel body is not analyzed in detail. In this paper, we investigate this with various fill types, such as annular, core-annular, and stratified fills at different positions. We compare the performance among several curve-fitting-based approaches and a machine-learning-based approach, the latter of which offers superior performance in estimating the fuel content. Full article

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety. Full article

Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results. Full article

In this paper, a new approach for capacitance measurement for electrical capacitance tomography (ECT) sensors is proposed. The method is based on the digital processing of the time-domain voltage measurements at the sensor electrodes. Furthermore, a robust capacitance estimation algorithm is developed to convert the measured voltage time-series to inter-electrode capacitances. The proposed measurement technique simplifies the electronic design of the ECT sensor and is suitable for use in applications requiring a compact device with a fast scan time. The accuracy and sensitivity of the method are investigated numerically and experimentally using a prototype sensor. In particular, the sensitivity of the estimated capacitance to measurement noise levels is analyzed in detail. Additionally, an analysis of the parameters that affect the accuracy of estimated capacitances is carried out from which we are able to demonstrate that the method is immune to effects such as stray capacitances between the electrodes and the ground. A prototype sensor with an open curved geometry on a millimeter scale is used to test the method empirically. Experimental results obtained for measurements with mineral oil and water are shown and compared against capacitances obtained using a physics-based forward model of the sensor. The inter-electrode capacitances in the range of tens of femtofarads to a few picofarads are estimated and a close match with the forward model results is obtained. Full article

The efficiency of a fluidized bed reactor depends on the bed fluid dynamic behavior, which is significantly influenced by the bubble properties. This work investigates the bubble properties of a bubbling fluidized bed reactor using computational particle fluid dynamic (CPFD) simulations and electrical capacitance tomography (ECT) measurements. The two-dimensional images (along the reactor horizontal and vertical planes) of the fluidized bed are obtained from the CPFD simulations at different operating conditions. The CPFD model was developed in a commercial CPFD software Barracuda Virtual Reactor 20.0.1. The bubble behavior and bed fluidization behavior are characterized form the bubble properties: average bubble diameter, bubble rise velocity, and bubble frequency. The bubble properties were determined by processing the extracted images with script developed in MATLAB. The CPFD simulation results are compared with experimental data (obtained from the ECT sensors) and correlations in the literature. The results from the CPFD model and experimental measurement depicted that the average bubble diameter increased with an increase in superficial gas velocities up to $4.2 U_{mf}$ and decreased with a further increase in gas velocities due to the onset of large bubbles (potential slugging regime). The bubble rise velocity increased as it moved from the lower region to the bed surface. The Fourier transform of the transient solid volume fraction illustrated that multiple bubbles pass the plane with varying amplitude and frequency in the range of 1–6 Hz. Further, the bubble frequency increased with an increase in superficial gas velocity up to $2.5U_{mf}$ and decreased with a further increase in gas velocity. The CPFD model and method employed in this work can be useful for studying the influence of bubble properties on conversion efficiency of a gasification reactor operating at high temperatures. Full article

An electrical capacitance tomography (ECT) sensor is an array capacitive sensor that is sensitive to the medium of the measured object and can be widely used in oil, natural gas, machinery and other industrial fields to solve the problem of multiphase fluid object parameter detection in industrial processes. However, ECT sensor uniformity defects need to be addressed. Aiming at the problem that the non-uniform electrode spacing angle affects the characteristics of the measured value of the actual ECT sensor, a method for compensating the measured value of the ECT sensor based on the assumption that the geometric factor is invariant is proposed. The simulated measurement value in the case of the tube and the measurement value of the actual sensor in the case of an empty tube and a full tube are to compensate the measurement value of the actual ECT. Experiments show that, without compensation, non-ideal sensor monitoring has large error, and, after compensation, it has a good effect and can be expected to perform satisfactorily in practical use. Full article

A desirable feature of an electrical capacitance tomography system is the adaptation possibility to any sensor configuration and measurement mode. A run-time reconfiguration of a system for electrical capacitance tomography is presented. An original mechanism is elaborated to reconfigure, on the fly, a modular EVT4 system with multiple FPGAs installed. The outlined system architecture is based on FPGA programmable logic devices (Xilinx Spartan) and PicoBlaze soft-core processors. Soft-core processors are used for communication, measurement control and data preprocessing. A novel method of FPGA partial reconfiguration is described, in which a PicoBlaze soft-core processor is used as a reconfiguration controller. Behavioral reconfiguration of the system is obtained by providing run-time access to the program code of a soft-core control processor. The tests using EVT4 hardware and different algorithms for tomographic scanning were performed. A test object was measured using 2D and 3D sensors. The time and resources required for the examined reconfiguration procedure are evaluated. Full article

Pneumatic conveying is a standard transportation technique for bulk materials in various industrial fields. Flow metering is crucial for the efficient and reliable operation of such systems and for process control. Capacitive measurement systems are often proposed for this application. In this method, electrodes are placed on the conveyor systems transport line and capacitive signals are sensed. The design of the sensor with regard to the arrangement and the number of electrodes as well as the evaluation of the capacitive sensor signals can be divided into two categories. Calibration-based flow meters use regression methods for signal processing, which are parametrized from calibration measurements on test rigs. Their performance is limited by the extend of the calibration measurements. Electrical capacitance tomography based flow meters use model-based signal processing techniques to obtain estimates about the spatial material distribution within the sensor. In contrast to their calibration-based counterparts, this approach requires more effort with respect to modeling and instrumentation, as typically a larger number of measurement signals has to be acquired. In this work we present a comparative analysis of the two approaches, which is based on measurement experiments and a holistic system model for flow metering. For the model-based analysis Monte Carlo simulations are conducted, where randomly generated pneumatic conveying flow patterns are simulated to analyze the sensor and algorithm behavior. The results demonstrate the potential benefit of electrical capacitance tomography based flow meters over a calibration-based instrument design. Full article

Gas-solid flow is used in the chemical industry, food industry, pharmaceuticals, vehicles, and power generation. The calculation of flow has aroused great interest in contemporary industry. In recent decades, researchers have been seeking to build an effective system to monitor and calculate gas-solid flow. Attempts have been extended from computational modeling to the creation of flow pattern visualization methods and mass flow (MFR) quantification. MFR is usually studied by volume flow concentration (VFC) and velocity distribution of solid particles. A non-invasive device is used for testing MFR, in which electronic and mechanical sensors are used to balance the shortcomings related to each other. This study investigates the simulation of flow patterns to demonstrate the behavior of solid particles as they pass through the channel. The particles are allowed to slide longitudinally in the insulated tending channel. This slippage is due to the influence of natural gravity. Electronic sensor components are used to measure the velocity distribution and concentration of volumetric flow. The load cell is used as an auxiliary sensor for measuring MFR. In addition, ANSYS fluent is used to analyze streaming queries. The experimental results are related to evaluating the accuracy and relative error of the data collected from various sensors under different conditions. However, the simulation results can help explain the movement of the gas-solid mixture and can understand the cause of pipeline blockage during the slow movement of solid particles. Full article

The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power levels of the microwave sources. A moisture controller can be designed to achieve this goal; however, a process model is required to design model-based controllers. Since complex physics governs the microwave drying process, system identification tools are employed in this paper to exploit the process input and output information and find a simplified yet accurate model of the process. The moisture content of the foam that is the process output is measured using a designed electrical capacitance tomography (ECT) sensor. The ECT sensor estimates the 2D permittivity distribution of moving foams, which correlates with the foam moisture. Experiments are conducted to collect the ECT measurements while giving different inputs to the microwave sources. A state-space model is estimated using one of the collected datasets and is validated using the other datasets. The comparison between the model response and the actual measurements shows that the model is accurate enough to design a controller for the microwave drying process. Full article