Visualisation of gas–oil two-phase flows in pressurised pipes using electrical capacitance tomography

Multiphase flow metering is a challenging task because of the complexity of multiphase flow. In this paper, nonintrusive multiphase flow metering techniques, including machine learning (ML) / artificial intelligence models for the identification of flow regimes and estimation of flow parameters of a two-phase flow in a horizontal pipe are proposed that use data from Electrical Capacitance Tomography (ECT) and conventional measurements such as differential pressure in the pipe. The flow regimes are classified into five types, namely plug, slug, annular, wavy and stratified. Two-phase air/water flow experimental data from ECT are collected by running extensive experiments using the horizontal section of the multiphase flow rig at the University of South-Eastern Norway (USN). Exploratory data analysis (EDA) is performed on these data to extract features for use in classification and regression algorithms. Time series of normalized capacitance data from ECT sensors are used to classify flow regimes and identify flow parameters. ML techniques of Artificial Neural Network, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are used to classify flow regimes by using features extracted from ECT data. The cross-correlation technique is used to estimate flow velocity using data from a twinplane ECT module. ML regression techniques are used to estimate phase fractions. Fusing data from differential pressure sensors enhances the flow regime classification. An overall system performance is given with suggestions for designing dedicated control algorithms for actuators used in multiphase flow control.

1 - Electrical Capacitance Tomography

The paper presents results of measurements of porosity changes in sand during granular flow in model silos using two different non‐invasive methods, i.e., Electrical Capacitance Tomography (ECT) and Particle Image Velocimetry (PIV). The experiments in model silos are carried out with different initial unit weights of sand and silo wall roughness. Advantages and disadvantages of both methods are outlined.

Investigation of wetting and drying process in a spout-fluid bed using acoustic sensor and electrical capacitance tomography

An 8-electrode electrical capacitance tomography (ECT) sensor located at the venturi throat on a vertical pipeline is used to investigate the flow regime of gas-oil two-phase flow. The experiment was carried out with the gas volume fraction (GVF) varies from 10% to 90% at different flow rates of oil. Slug flow appears at a low GVF. While with a high GVF, a clear annular flow will be formed. For different flow rates of oil, the flow regime at the same GVF point could be different because of the flow rate of gas (or superficial velocity of gas). A method independent of flow regime is developed to calculate the phase fraction of gas-oil two-phase flows. The absolute error can be within ±10%, illustrating that this method is effective.

Measurement techniques are vital for the control and operation of multiphase oil–water flow in pipes. The development of such techniques depends on laboratory experiments involving flow visualization, liquid fraction (‘hold-up’), phase slip and pressure drop measurements. They provide valuable information by revealing the physics, spatial and temporal structures of complex multiphase flow phenomena.This paper presents the hold-up measurement of oil–water flow in pipelines using gamma densitometry and electrical capacitance tomography (ECT) sensors. The experiments were carried out with different pipe inclinations from −5° to +6° for selected mixture velocities (0.2–1.5 m s−1), and at selected watercuts (0.05–0.95). Mineral oil (Exxsol D60) and water were used as test fluids. Nine flow patterns were identified including a new pattern called stratified wavy and mixed interface flow. As a third direct method, visual observations and high-speed videos were used for the flow regime and interface identification.ECT and gamma densitometry hold-up measurements show similar trends for changes in pipeline inclinations. Changing the pipe inclination affected the flow mostly at lower mixture velocities and caused a change of flow patterns, allowing the highest change of hold-up. ECT hold-up measurements overpredict the gamma densitometry measurements at higher input water cuts and underpredict at intermediate water cuts. Gamma hold-up results showed good agreement with the literature results, having a maximum deviation of 6%, while it was as high as 22% for ECT in comparison to gamma densitometry.Uncertainty analysis of the measurement techniques was carried out with single-phase oil flow. This shows that the measurement error associated with gamma densitometry is approximately 3.2%, which includes 1.3% statistical error and 2.9% error identified as electromagnetically induced noise in electronics. Thus, gamma densitometry can predict hold-up with a higher accuracy in comparison to ECT when applied to oil–water systems at minimized electromagnetic noise.

Visualization of flow pattern in thermosyphon by ECT

This article describes the recent progress in research and development on electrical capacitance tomography (ECT). Specifically, the article highlights several aspects of ECT including the electrical capacitance volume tomography (ECVT), 3D sensor design, 3D neural network multicriterion image reconstruction technique (3D-NN-MOIRT), multimodal imaging based on ECT and ECVT sensors, static-charge effects and the scheme of their elimination in the ECT image reconstruction, and multiphase flow imaging applications. The multimodal capability that enables permittivity and conductivity imaging to be simultaneously conducted is illustrated. The simulation and experimental results are presented to provide quantitative and/or qualitative assessment of the significance of various ECT techniques. The employment of ECVT in conjunction with using electrical capacitance based imaging sensors is shown to represent a favorable tool for industrial multiphase flow imaging.

Electrical capacitance tomography (ECT) is a non-invasive measurement technique widely used for two-phase flow imaging and parameter measurement. Image reconstruction of ECT analyzes the capacitance measurements from the ECT sensor and reconstructs the permittivity distribution in the sensing domain through certain algorithms. Due to its ill-posedness, image reconstruction has always been a hotspot and a challenge in ECT research. Over the past decade, the blooming of deep learning has introduced promising avenues for addressing this challenge. Numerous deep learning-based models and algorithms have been developed for ECT image reconstruction, and remarkable achievements have been made. This paper comprehensively summarizes the state-of-the-art deep learning approaches for ECT image reconstruction. In addition, the challenges and future directions of deep learning-based ECT image reconstruction are also discussed in perspective.

Characterizing the liquid dynamics in cocurrent gas–liquid flows in porous media using twin-plane electrical capacitance tomography

An image reconstruction algorithm based on the semiparametric model for electrical capacitance tomography

Most image reconstruction algorithms developed for electrical capacitance tomography (ECT) can only reconstruct qualitative images. Stabled quantitative image reconstruction is necessary for many applications. To get stable ECT image, the authors constructed a compressive operator and developed a new iterative algorithm, which can overcome the semi-convergence occurring in the Landweber iteration reconstruction technique. Experimental results showed that the stability and quality of reconstructed images are improved significantly.

To improve the quality of the reconstruction image for discontinuous media distribution areas, a total variation (TV) regularization based on bounded variation function was employed to solve the ill-posed problem of electrical capacitance tomography (ECT). By using TV regularization, an Euler equation, a non-linear partial differential equation, is derived to describe the ECT problem. However, it is very time consuming, if possible, for traditional methods to solve this Euler equation. This paper presents a new fixed-point iterative algorithm which can get the solution stably and rapidly. Numerical results show that the presented method improves the quality of the reconstruction image for discontinuous media distribution areas in ECT image reconstruction, and leads to the common boundary between different media clearer.Keywordselectrical capacitance tomography (ECT)image reconstructionill-posedtotal variation regularizationfixed-point iterative algorithm

We have evaluated the usefulness of electrical capacitance tomography (ECT) as a tool for monitoring pneumatic conveying in horizontal ducts. Power spectra of solids concentration fluctuations obtained from single-plane ECT data were used to identify the various flow regimes, and these were confirmed through visual observation. From single-plane ECT data, the instantaneous and time-averaged distributions of particle concentration over the cross section of the conveying pipe have been determined in various flow regimes. Propagation velocities of patterns were evaluated from cross correlation of twin-plane ECT data. The solids mass flow rate, determined independently by load cell measurements, was found to be roughly proportional to the product of the pattern velocity, the particle density, and the average solids holdup in the pipe, and the proportionality factor depended on the material being transported through the pipe. In our experiments involving flows past a 90° smooth bend, ECT was able to detect significant temporal and spatial nonuniformity in particle concentration in the postbend region.

CFD-DEM study of gas-solid flow regimes in a Wurster type fluidized bed with experimental validation by electrical capacitance tomography