Tomography Sensor Research Articles

Improving the reconstruction accuracy of tomographic absorption spectroscopy sensor by optimizing laser beam arrangements

Tomographic absorption spectroscopy (TAS) typically employs multiple projections to reconstruct the spatial distribution of temperature and species concentration, making it a promising method for combustion diagnostics. However, the geometric arrangement of the laser beams significantly affects its accuracy, especially under limited beam conditions. In this work, we propose a beam arrangement optimization method to maximize the utilization efficiency of limited beams, thereby improving reconstruction accuracy in a simpler and more intuitive manner. We designed a cost function that combines the beam number matrix (BNM) and the total weight matrix (TWM). This approach ensures that the maximum number of rays passes through each grid while also maximizing the optical path length in the regions covered by the beam distribution. The optimization is performed using a simulated annealing algorithm to obtain the optimal beam arrangement. For a TAS sensor comprising 12 laser beams, numerical simulations demonstrate that the BNM-TWM optimized beam arrangement achieves lower reconstruction errors across various synthetic temperature fields when compared to traditional orthogonal beam arrangements and those optimized for orthogonality (OD). This improvement is particularly significant when the performance of the orthogonal and OD-optimized arrangements is suboptimal. Finally, we demonstrate the application of the BNM-TWM optimized beam arrangement for reconstructing temperature distributions of asymmetric butane flames, where parallel beam distributions were ineffective. The results effectively pinpoint the location of the flame, showing a temperature difference of less than 3% compared to thermocouple measurements for the center of the flame. These findings indicate that the developed beam arrangement optimization method has the potential to enhance the accuracy of TAS reconstruction under limited beam conditions and could be extended to other tomographic imaging systems.

High-resolution 4D electrical resistivity tomography and below-ground point sensor monitoring of High Arctic deglaciated sediments capture zero-curtain effects, freeze–thaw transitions, and mid-winter thawing

Abstract. Arctic regions are under immense pressure from a continuously warming climate. During the winter and shoulder seasons, recently deglaciated sediments are particularly sensitive to human-induced warming. Understanding the physical mechanisms and processes that determine soil liquid moisture availability contributes to the way we conceptualize and understand the development and functioning of terrestrial Arctic ecosystems. However, harsh weather and logistical constraints limit opportunities to directly observe subsurface processes year-round; hence automated and uninterrupted strategies of monitoring the coupled heat and water movement in soils are essential. Geoelectrical monitoring using electrical resistivity tomography (ERT) has proven to be an effective method to capture soil moisture distribution in time and space. ERT instrumentation has been adapted for year-round operation in high-latitude weather conditions. We installed two geoelectrical monitoring stations on the forefield of a retreating glacier in Svalbard, consisting of semi-permanent surface ERT arrays and co-located soil sensors, which track seasonal changes in soil electrical resistivity, moisture, and temperature in 3D. One of the stations observes recently exposed sediments (5–10 years since deglaciation), whilst the other covers more established sediments (50–60 years since deglaciation). We obtained a 1-year continuous measurement record (October 2021–September 2022), which produced 4D images of soil freeze–thaw transitions with unprecedented detail, allowing us to calculate the velocity of the thawing front in 3D. At its peak, this was found to be 1 m d−1 for the older sediments and 0.4 m d−1 for the younger sediments. Records of soil moisture and thermal regime obtained by sensors help define the conditions under which snowmelt takes place. Our data reveal that the freeze–thaw shoulder period, during which the surface soils experienced the zero-curtain effect, lasted 23 d at the site closer to the glacier but only 6 d for the older sediments. Furthermore, we used unsupervised clustering to classify areas of the soil volume according to their electrical resistivity coefficient of variance, which enables us to understand spatial variations in susceptibility to water-phase transition. Novel insights into soil moisture dynamics throughout the spring melt will help parameterize models of biological activity to build a more predictive understanding of newly emerging terrestrial landscapes and their impact on carbon and nutrient cycling.

Gas–liquid two-phase flow measurement by using electrical tomography sensors and Venturi

Gas–liquid two-phase flow is characterized by complexity, non-homogeneity and difficulty in modeling. Therefore, traditional measurement methods often struggle to simultaneously meet the requirements of high precision and a wide measurement range. In this context, this study aims to explore an innovative multi-sensor fusion technique to achieve accurate measurement of gas–liquid two-phase flow under full operating conditions. In this study, a combination of three sensors, Venturi, ERT (Electrical Resistance Tomography) and ECT (Electrical Capacitance Tomography), is used to propose a new method based on the fusion of LVF (Liquid-phase Volume Fraction) calculations with flow results. Firstly, the high sensitivity characteristics of ECT and ERT sensors are utilized to obtain spatial distribution information of the flow, which is then used to calculate the LVF. Subsequently, the calculated LVF is combined with the total flow rate measured by the Venturi. Through an established mathematical model, the individual flow rates of the gas and liquid phases are inversely calculated. The results show that the method exhibits high measurement accuracy in different LVF ranges with the relative error controlled within 10%, especially in wet and non-wet gas conditions, which fills the gap of the traditional methods in this field. In addition, the method not only improves the measurement accuracy, but also broadens the measurement range, which meets the diversified needs of complex two-phase flow measurement in the oil and gas industry.

A flow rate estimation method for gas–liquid two-phase flow based on filter-enhanced convolutional neural network

Accurate estimation of flow rate in gas–liquid two-phase flow is crucial for various industrial processes. How to accurately estimate flow rate remains a challenging problem. Previously, deep learning-based methods focused on a few human-set points with single task learning. In addition, the data were not denoised. In this study, a flow rate estimation method based on a filter-enhanced convolutional neural network (FECNN) is proposed for gas–liquid two-phase flow. The method leverages multimodal data from a Venturi tube and an electrical capacitance tomography (ECT) sensor as input, utilizing multilayer perceptron (MLP) to fuse data. Subsequently, a learnable filter module is employed to attenuate noise adaptively, followed by multiscale convolutional neural network (MSCNN) extraction of flow rate features at different scales. Finally, the method enables estimate each single-phase flow rate simultaneously through multi-task learning (MTL). The adaptive noise attenuation capabilities of the learnable filter module are demonstrated, and the ability of the proposed MSCNN to capture multiscale flow rate features through multiple comparative experiments is shown. Additionally, a qualitative comparison with recent flow rate estimation methods is provided. Overall, this study demonstrates the effectiveness and superiority of the proposed FECNN in flow rate estimation.

Combing mobile electrical capacitance tomography with Fourier neural operator for 3D fluidized beds measurement

AbstractDespite the practical importance, 3D measurements of gas–solid distribution in fluidized beds calls for further breakthroughs. Here an approach combing a recently developed mobile electrical capacitance tomography (ECT) sensor with Fourier Neural Operator (FNO) is developed, in which the fluidized bed is divided into a series of cross‐sectional slices along axial direction. At any given instant, the gas–solid distribution in one slice is measured by mobile ECT and the others, meantime, are predicted by FNO pre‐trained using experimental data. We verified this approach via computational fluid dynamics (CFD) simulations and experimental measurement of static object (i.e., cone, cylinder, and sphere) in fluidized bed. Following we applied this approach to direct measure 3D gas–solid distribution in a bubbling fluidized bed, and found that satisfactory image correlation coefficients and solid concentration average absolute deviation could be obtained, which indicates the proposed approach is promising for 3D fluidized bed measurements.

Real-time monitoring and control of the layer height in laser metal deposition with coaxial wire feeding using optical coherence tomography

Laser metal deposition (LMD) with coaxial wire feeding is an additive manufacturing technology in which a metal wire is fed into a laser-induced melt pool. The repeated deposition of weld beads allows three-dimensional geometries to be created that can be used for manufacturing, repair, and modification of metal components. However, the process is highly sensitive to disturbances because the fed wire must always be fully melted, and no self-regulating effects as in powder-based LMD exist. The layer height is particularly important for process stability, as even small deviations accumulate over many layers and, ultimately, lead to the termination of the process. Therefore, monitoring and closed-loop control of the layer height during the deposition process are crucial. Due to process emissions, an interruption of the process is usually necessary for the accurate optical measurement of the layer height, which negatively affects the overall productivity. In order to overcome this drawback, an in-axis optical coherence tomography (OCT) sensor was employed in this work, which enabled real-time measurements of the layer height. It was found that positioning the OCT measurement spot as close as possible to the center of the wire provided the highest signal quality. Based on the real-time height data, a closed-loop layer height control was implemented, applying the wire feed rate as the manipulated variable. The experimental results showed that the proposed system was able to compensate for significant disturbances, ensuring dimensional accuracy and process stability.

Electrical Sensor Calibration by Fuzzy Clustering with Mandatory Constraint.

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.

Exploration of Electrical Impedance Tomography and Grid-Based Soft Sensors for Distributed, Large Deformation Strain Sensing Based on a Fiber-Elastomer Composite

Exploration of Electrical Impedance Tomography and Grid-Based Soft Sensors for Distributed, Large Deformation Strain Sensing Based on a Fiber-Elastomer Composite

Determination of optimal positive end-expiratory pressure using electrical impedance tomography in infants under general anesthesia: Comparison between supine and prone positions.

This study determined the optimal positive end-expiratory pressure levels in infants in supine and prone positions under general anesthesia using electrical impedance tomography (EIT). This prospective observational single-centre study included infants scheduled for surgery in the prone position. An electrical impedance tomography sensor was applied after inducing general anesthesia. The optimal positive end-expiratory pressure in the supine position was determined in a decremental trial based on EIT and compliance. Subsequently, the patient's position was changed to prone. Electrical impedance tomography parameters, including global inhomogeneity index, regional ventilation delay, opening pressure, the centre of ventilation, and pendelluft volume, were continuously obtained up to 1 h after prone positioning. The optimal positive end-expiratory pressure in the prone position was similarly determined. Data from 30 infants were analyzed. The mean value of electrical impedance tomography-based optimal positive end-expiratory pressure in the prone position was significantly higher than that in the supine position [10.9 (1.6) cmH2O and 6.1 (0.9) cmH2O, respectively (p < .001)]. Significant differences were observed between electrical impedance tomography- and compliance-based optimal positive end-expiratory pressure. Peak and mean airway, plateau, and driving pressures increased 1 h after prone positioning compared with those in the supine position. In addition, the centre of ventilation for balance in ventilation between the ventral and dorsal regions improved. The prone position required higher positive end-expiratory pressure than the supine position in mechanically ventilated infants under general anesthesia. EIT is a promising tool to find the optimal positive end-expiratory pressure, which needs to be individualized.

Axial behavior of slugging flow in a gas–solid CFB riser using distributed ECVT with 64 electrodes

Understanding particle flow characteristics within circulating fluidized bed (CFB) reactors is crucial for enhancing reaction efficiency. To study the flow characteristics of Geldart D particles with a mean diameter of 3.0 mm and a bulk density of 1640 kg/m3 in a CFB riser with a height of 1090 mm, a 64-electrode electrical capacitance volume tomography (ECVT) sensor with a height-to-diameter ratio (HDR) of 12 is arranged on the CFB riser with an inner diameter of 59 mm. A distributed ECVT architecture under the control of a timing controller is formed to synchronously acquire capacitance data of two 32-electrode ECVT data acquisition systems. So, the three-dimensional axial flow imaging of a CFB riser with a branch pipe is realized with a 14-plane ECVT sensor. The flow rules of slugging flow in the CFB riser under gas velocities of 3.5 m/s, 2.86 m/s, and 2.5 m/s are analyzed from the continuous ECVT images, especially the process of rupture, decomposition, and collapse of particle slugs. The flow parameter characteristics, such as the solid fraction in the riser, the slug rising tail velocity, the slug length, and the spectral characteristics, are investigated. The experimental results demonstrate that the proposed distributed ECVT can effectively realize the collaborative operation of multiple ECVT data acquisition systems and then splice sub-ECT images to achieve complex space imaging.

Spectrometer as a quantitative sensor for predicting the weld depth in laser welding

Spectrometers have been used for quality assessment or as auxiliary sensors in multisensor systems to record emissions at different wavelengths. This study developed a single-spectrometer sensor to quantitatively predict the laser penetration depth in Cu/Al overlap and dual-phase (DP) steel bead-on-plate welding joints. The spectrometer signals were sampled at 100 Hz, and an optical coherence tomography (OCT) sensor was used to estimate the in situ penetration depth. A one-dimensional (1D) convolution neural network (CNN) model was developed using the current step of the spectrometer signal. Furthermore, a two-dimensional (2D) CNN model was developed using 10 recent steps of the spectrometer signals. Consequently, 1732 and 3000 data points for DP steel and Al/Cu overlap welding were used for the penetration-depth modeling. Through model training, mean absolute errors of 22.67 and 52.96 µm were achieved for the Al/Cu overlap and DP steel welding, respectively, corresponding to approximately 1.5 % of the substrate thickness. The 2D-CNN models outperformed the 1D-CNN models for both material combinations. However, a slight delay was observed in the response of the models when confronted with abrupt output changes. This study confirmed that spectroscopy can serve as the sole quantitative sensing method for laser welding monitoring and control.

Dynamic bubble tracking in fluidized beds via electrical capacitance volume tomography

This work sets forth the development of a novel, dynamic bubble detection algorithm for use with electrical capacitance volume tomography (ECVT) sensors for bubbling fluidized beds. Starting with an in-depth review of existing bubble detection methods, the novel phase detection method was developed to address the shortcomings of the other published methods by more fully utilizing the three-dimensional capability of the sensors. After the sensor parameters were optimized to verify capability of bubble detection, measurements were made with glass beads and quartz sand. A range of air velocities in a 10 cm diameter bubbling fluidized bed were used. The results largely agree with the fundamentals of bubbling fluidization and results from publications with similar experimental setups. Within each measurement the bubble dimensions, velocities and frequencies over the axial and radial position could be evaluated. Due to the three-dimensional nature of the novel bubble detection technique, insight into the directional tendencies of detected bubbles was gained. For example, bubble migration toward the radial center of the bed, radial and axial bubble coalescence, and splitting are more evident from the trends that are producible with this method.

Measurement of the water-to-liquid ratio of oil–water two-phase flow for low flow rates and high water content

Oil–water two-phase flow is widely present in the petroleum industry, and the vast majority of oilfields have entered the period of exploitation under high water content. Accurate measurement of water-to-liquid ratio (WLR) is crucial to oil production. Currently, there are fewer works related to the WLR measurement at high water content and low flow rates. In order to get a complete picture of WLR measurement in this context by electrical resistance tomography (ERT) sensors, dynamic experiments with a total flow rate not exceeding 4m3h−1 were conducted. These dynamic experiments considered the influence of the direction of the test pipelines, the inner diameter of the ERT sensor and the conductivity of the water. According to the relative positions of the measurement and excitation electrode pairs of the ERT sensor, different strategies for WLR calculation were established. This paper presents all results of the experiments of the WLR measurement. Especially, image reconstruction at different flow rates was conducted, as a way to qualitatively reflect the mixing of oil and water, thus helping to understand the WLR measurements. The whole work reveals the regularities of the WLR measurement by ERT, and provides a reference for subsequent work to realize the WLR accurate measurement in oil-water two-phase flow at high water content and low flow rates.

On-line monitoring of membrane fouling based on an improved electrical measurement method.

On-line monitoring of membrane fouling is essential in the water treatment process. Drawbacks such as low-sensitivity and off-line limitations limit the application of existing methods. An on-line monitoring method based on Electrical Resistance Tomography (ERT) sensors is put forward in this paper. The Particle Swarm Optimization with Simulated Annealing (PSO-SA) algorithm is used in optimizing the topologies of finite element models in order to decrease the ill-posedness of sensitivity matrices. The deep denoising extreme learning machine with an auto-encoder model and the K-singular value decomposition algorithm are used in ERT reconstruction to improve imaging quality. The lift-wavelet is adopted in measuring the permeate flux to improve measuring accuracy. The ERT pixel values of the membrane module and the result of flux are used to analyze the fouling status. The results of membrane fouling experiments demonstrate the following: (1) Based on the local ERT pixels, the "two stage" phenomenon of membrane fouling can be observed. (2) In the early stage, the fouling distribution of the localized membrane module is consistent with its ERT pixels. (3) The deposition process of foulants for the localized membrane module is synchronized with the variation of ERT pixels. (4) The integrity of the membrane module can be detected according to the ERT pixels. Therefore, the novel method can effectively reflect the membrane fouling process, especially in the early stages of membrane fouling.

Online defect detection on metallic plates using electromagnetic tomography

Metallic samples are widely applied in modern industrial production. Due to non-uniformities in the stress load, such samples may become damaged and produce defects, which can cause unnecessary economic losses. In this paper, an online defect detection method is proposed for the quality monitoring of metallic plates. The research involves the design and optimisation of an electromagnetic tomography (EMT) sensor and the development of a fast tomography algorithm. Specifically, a planar array eddy current sensor is designed for in-situ structural health monitoring of metallic specimens. The parameters of the sensor are optimised using an orthogonal methodology and a response surface methodology to improve the uniformity of the sensitivity field. In addition, a second-order iterative Bregman reconstruction algorithm is investigated to reconstruct the defect image, which can improve the reconstruction speed for this ill-posed problem. Simulation and experimental results indicate that the proposed method can be applied to effectively evaluate the locations and sizes of defects in metallic specimens. The correlation coefficients of the reconstructed images using the proposed method are larger than 0.8. Compared with traditional reconstruction algorithms, the method proposed in this paper shows fast convergence speed and smaller estimation errors.

Cross-Domain Retinopathy Classification Based on Optical Coherence Tomography Sensors via Domain Adversarial Graph Convolutional Network

Cross-Domain Retinopathy Classification Based on Optical Coherence Tomography Sensors via Domain Adversarial Graph Convolutional Network

Algorithms for Optimizing Energy Consumption for Fermentation Processes in Biogas Production

Problems related to reducing energy consumption constitute an important basis for scientific research worldwide. A proposal to use various renewable energy sources, including creating a biogas plant, is emphasized in the introduction of this article. However, the indicated solutions require continuous monitoring and control to maximise the installations’ effectiveness. The authors took up the challenge of developing a computer solution to reduce the costs of maintaining technological process monitoring systems. Concept diagrams of a metrological system using multi-sensor techniques containing humidity, temperature and pressure sensors coupled with Electrical Impedance Tomography (EIT) sensors were presented. This approach allows for effective monitoring of the anaerobic fermentation process. The possibility of reducing the energy consumed during installation operation was proposed, which resulted in the development of algorithms for determining alarm states, which are the basis for controlling the frequency of technological process measurements. Implementing the idea required the preparation of measurement infrastructure and an analytical engine based on AI techniques, including an expert system and developed algorithms. Numerous time-consuming studies and experiments have confirmed reduced energy consumption, which can be successfully used in biogas production.

Miniature planar 3D ECT sensor based on distributed electrode arrangement

Due to the low sensitivity in the middle of sensing chamber of planar peripheral electrode arrangement tomography system, a miniature planar electrical capacitance tomography (ECT) sensor with distributed electrode arrangement is proposed for imaging of 3D cultivated cells. In this work, the 3D imaging capability of the proposed sensor was compared to the peripheral electrode arrangement ECT sensor through numerical simulation of the sensitivity maps. The analysis of the sensitivity maps revealed that the sensitivity distribution of proposed sensor covered the entire surface of the sensing chamber which indicated improved sensitivity coverage in the sensing chamber. In addition, the axial resolution and the singular value decomposition (SVD) analysis of the sensitivity maps demonstrated that the proposed sensor has better 3D imaging capability. Then, the proposed sensor composed of nine electrodes, a thin PDMS layer and a glass coverslip was fabricated and investigated. The experimental results showed that the reconstructed 3D image by the proposed sensor was consistent at all the test positions. In contrast to the planar peripheral electrode arrangement ECT sensor that has an average correlation coefficient (CC) of 0.4571, the proposed sensor achieved a higher average CC of 0.5454, indicating better 3D imaging performance.

3D Image Reconstruction Capability of Peripheral On-chip ECT Sensor

The miniaturisation of electrical tomography sensors such as EIT has become popular recently and these miniaturised sensors were investigated for 2D and 3D imaging of cells in EIT. Nevertheless, research on miniaturisation of ECT sensor also existed which were based on peripheral electrode sensor which had electrodes arranged on the surface around the sensing chamber. However, the study on the miniature peripheral ECT sensor was limited to 2D image reconstruction and detection of presents of samples in the sensing chamber only and lacked the works on the 3D imaging capability. Therefore, this article investigates the 3D image reconstruction capability of peripheral on-chip ECT sensor through simulation approach by simulating a cube phantom at several test positions in the sensing chamber. Eventually, the 3D and 2D images were reconstructed and the 3D imaging capability of the sensor was characterised qualitatively based on visual inspection of the reconstructed image. The simulation results revealed that the 3D imaging capability of peripheral on-chip ECT sensor was limited to the region where electrodes were present only. However, the 2D reconstructed image results showed that the sensor was able to locate the positions of test phantom in the sensing chamber accurately.

Heterogeneous grain growth and vertical mass transfer within a snow layer under a temperature gradient

Abstract. Inside a snow cover, metamorphism plays a key role in snow evolution at different scales. This study focuses on the impact of temperature gradient metamorphism on a snow layer in its vertical extent. To this end, two cold-laboratory experiments were conducted to monitor a snow layer evolving under a temperature gradient of 100 K m−1 using X-ray tomography and environmental sensors. The first experiment shows that snow evolves differently in the vertical: in the end, coarser depth hoar is found in the center part of the layer, with covariance lengths about 50 % higher compared to the top and bottom areas. We show that this heterogeneous grain growth could be related to the temperature profile, to the associated crystal growth regimes, and to the local vapor supersaturation. In the second experiment, a non-disturbing sampling method was applied to enable a precise observation of the basal mass transfer in the case of dry boundary conditions. An air gap, characterized by a sharp drop in density, developed at the base and reached more than 3 mm after a month. The two reported phenomena, heterogeneous grain growth and basal mass loss, create heterogeneities in snow – in terms of density, grain and pore size, and ice morphology – from an initial homogeneous layer. Finally, we report the formation of hard depth hoar associated with an increase in specific surface area (SSA) observed in the second experiment with higher initial density. These microscale effects may strongly impact the snowpack behavior, e.g., for snow transport processes or snow mechanics.