Dual-Modality 4-Terminal Electrical Capacitance and Resistance Tomography for Multiphase Flow Monitoring

Frias, Marco A. Rodriguez; Yang, Wuqiang

doi:10.1109/jsen.2019.2955399

Cited by 18 publications

(8 citation statements)

References 34 publications

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“…The method for liquid hold-up calculation can be used across different packed column zones with different flow patterns. However, it has previously been reported that the error of ECT starts to increase sharply at 60% liquid hold-up [39]. We expect this Maxwell equation-based method to be appropriate with gas phase dominant flow at low gas flow rates, but that it will inevitably cause large errors when the packed column is flooding.…”

Section: B Electrical Capacitance Tomography Sensor Calibration and L...mentioning

confidence: 97%

“…Therefore, ECT must operate with a liquid phase dominant absorption mass transfer operation to monitor flooding. However, ECT remains unreliable for measuring liquid phase dominant absorption mass transfer operation as the error for the ECT modality starts to increase sharply at 60% liquid hold-up [39]. Secondly, the position of the ECT sensor must be adjusted appropriately to monitor the relationship between local liquid hold-up and flooding.…”

Section: Introductionmentioning

confidence: 99%

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Flooding Prognostic in Packed Columns Based on Electrical Capacitance Tomography and Convolution Neural Network

Chen

Liu

Yang

et al. 2022

IEEE Trans. Instrum. Meas.

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The flooding of packed columns is accompanied by a steep increase in liquid hold-up and pressure drop, resulting in lower mass transfer efficiency and potential damage to equipment. This study aims to investigate, for the first time, the feasibility of Electrical Capacitance Tomography and Convolutional Neural Networks as an intensified alternative to conventional flooding prediction methods. Electrical Capacitance Tomography allows variations in the predominant characteristics of flooding events to be investigated in greater detail than in previous research. Combined with Convolutional Neural Networks, the Electrical Capacitance Tomography sensor enables high accuracy on liquid hold-up calculation and strong robustness against noisecontaminated measurements. In this work, a detailed comparison is made between liquid hold-up results using Convolutional Neural Networks and a more conventional Electrical Capacitance Tomography method based on Maxwell equation. Both methods can accurately calculate the liquid hold-up at low gas flow rates. The liquid hold-up predicted according to Maxwell equation did not match the measured values at high gas flow rates, showing discrepancies of up to 68%. In contrast, Convolutional Neural Networks is much superior to the Maxwell equation method at high gas flow rates, giving only 1% mean of difference than the reference liquid hold-up. Electrical Capacitance Tomography supported by Convolutional Neural Networks shows great fidelity for non-invasive monitoring of local liquid hold-up, allowing for more accurate, localized prediction of loading point and flooding point in packed columns.

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Section: B Electrical Capacitance Tomography Sensor Calibration and L...mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Flooding Prognostic in Packed Columns Based on Electrical Capacitance Tomography and Convolution Neural Network

Chen

Liu

Yang

et al. 2022

IEEE Trans. Instrum. Meas.

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“…Microwave methods have the advantages of accurate measurement, easy installation and low external influence, but they are sensitive to noise and costly [10,11]. ET methods have the advantages of simplicity, speed, non-radiation, non-intrusive and low cost and so on [12], and can be applied to visualize the flow regimes via image reconstruction [13], so it can be widely used in real-time measurement of various industrial processes.…”

Section: Introductionmentioning

confidence: 99%

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

Huang,

Chen,

Song

et al. 2024

Meas. Sci. Technol.

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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.

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“…Electrical capacitance tomography (ECT) is a non-invasive electrical tomography technique that is widely used in industrial application such as fluidised bed (Porzuczek, 2014; Huang et al , 2019; Wang and Yang, 2021) and multiphase flow measurement (Yao and Takei, 2017; Rodriguez-Frias and Yang, 2020; Ye et al , 2020; Shen et al , 2021). The ECT reconstructs images based on the permittivity distribution by mapping the changes in interelectrode capacitance to the sensitivity distribution.…”

Section: Introductionmentioning

confidence: 99%

Dual-plane miniature planar 3D ECT sensor based on 3 × 3 matrix electrode array

Gooi,

Leow,

Pusppanathan

et al. 2023

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Purpose As one of the tomographic imaging techniques, electrical capacitance tomography (ECT) is widely used in many industrial applications. While most ECT sensors have electrodes placed around a cylindrical chamber, the planar ECT sensor has been investigated for depth and defect detection. However, the planar ECT sensor has limited height and depth sensing capability due to its single-sided assessment with the use of only a single-plane design. The purpose of this paper is to investigate a dual-plane miniature planar 3D ECT sensor design using the 3 × 3 matrix electrode array. Design/methodology/approach The sensitivity map of dual-plane miniature planar 3D ECT sensor was analysed using 3D visualisation, the singular value decomposition and the axial resolution analysis. Then, the sensor was fabricated for performance analysis based on 3D imaging experiments. Findings The sensitivity map analysis showed that the dual-plane miniature planar 3D ECT sensor has enhanced the height sensing capability, and it is less ill-posed in 3D image reconstruction. The dual-plane miniature planar 3D ECT sensor showed a 28% improvement in reconstructed 3D image quality as compared to the single-plane sensor set-up. Originality/value The 3 × 3 matrix electrode array has been proposed to use only the necessary electrode pair combinations for image reconstruction. Besides, the increase in number of electrodes from the dual-plane sensor setup improved the height reconstruction of the test sample.

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Dual-Modality 4-Terminal Electrical Capacitance and Resistance Tomography for Multiphase Flow Monitoring

Cited by 18 publications

References 34 publications

Flooding Prognostic in Packed Columns Based on Electrical Capacitance Tomography and Convolution Neural Network

Flooding Prognostic in Packed Columns Based on Electrical Capacitance Tomography and Convolution Neural Network

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

Dual-plane miniature planar 3D ECT sensor based on 3 × 3 matrix electrode array

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