Capacitive Sensors for Multiphase Flow Measurement: A Review

Wrasse, Aluísio do Nascimento; Santos, Eduardo Nunes dos; Silva, Marco J. da; Wu, Hao; Tan, Chao

doi:10.1109/jsen.2022.3210467

Cited by 16 publications

(4 citation statements)

References 154 publications

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“…In the foreseeable future, this approach could generate data to machine learning algorithms, rendering calibrations independent of specific fluid properties. This type of methodology aligns with the current trend, reflecting the progress of artificial intelligence in diverse processing routines across various domains in recent years [ 22 ]. Recently, a lot of investigations have done utilizing capacitance-based sensors and an Artificial Neural Network (ANN) to quantify volume fractions in a dual-phase fluid.…”

Section: Introductionmentioning

confidence: 57%

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors

Mayet,

Fouladinia,

Alizadeh

et al. 2024

PLoS ONE

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Many different kind of fluids in a wide variety of industries exist, such as two-phase and three-phase. Various combinations of them can be expected and gas-oil-water is one of the most common flows. Measuring the volume fraction of phases without separation is vital in many aspects, one of which is financial issues. Many methods are utilized to ascertain the volumetric proportion of each phase. Sensors based on measuring capacity are so popular because this kind of sensor operates seamlessly and autonomously without necessitating any form of segregation or disruption for measuring in the process. Besides, at the present moment, Artificial intelligence (AI) can be nominated as the most useful tool in several fields, and metering is no exception. Also, three main type of regimes can be found which are annular, stratified, and homogeneous. In this paper, volume fractions in a gas-oil-water three-phase homogeneous regime are measured. To accomplish this objective, an Artificial Neural Network (ANN) and a capacitance-based sensor are utilized. To train the presented network, an optimized sensor was implemented in the COMSOL Multiphysics software and after doing a lot of simulations, 231 different data are produced. Among all obtained results, 70 percent of them (161 data) are awarded to the train data, and the rest of them (70 data) are considered for the test data. This investigation proposes a new intelligent metering system based on the Multilayer Perceptron network (MLP) that can estimate a three-phase water-oil-gas fluid’s water volume fraction precisely with a very low error. The obtained Mean Absolute Error (MAE) is equal to 1.66. This dedicates the presented predicting method’s considerable accuracy. Moreover, this study was confined to homogeneous regime and cannot measure void fractions of other fluid types and this can be considered for future works. Besides, temperature and pressure changes which highly temper relative permittivity and density of the liquid inside the pipe can be considered for another future idea.

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Section: Introductionmentioning

confidence: 57%

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors

Mayet,

Fouladinia,

Alizadeh

et al. 2024

PLoS ONE

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“…Figure 10 clearly illustrates this process. To accurately detect the surface gap distance, it is necessary to use a connected component segmentation algorithm [17,18] to separate the different regions into distinct parts. The segmentation method used in this study, as illustrated in figure 11, which initially involves calculating the Top, Left, Right, and Down images using a four-neighbourhood Mask template and threshold T, namely:…”

Section: Point Cloud Pre-processingmentioning

confidence: 99%

Gap measurement method based on projection lines and convex analysis of 3D points cloud

Pan,

Jiang,

Tang

et al. 2024

Meas. Sci. Technol.

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Accurate measurement of the gap between the lower surface of the relay and the ground is critical for ensuring the quality of the finished product. Traditional gap measurement methods have some shortcomings, such as low accuracy, poor robustness, and loss of depth clues in obscured areas. In this study, a novel gap measurement method based on computer vision is proposed, which includes a projection line model based on guided filtering and a 3D surface point cloud model based on a three dimensional plane reference.- The relay gap was measured by calculating the projection lines of the upper and lower surfaces of the gap with an error of ±0.016 mm. A 3D point cloud model captures the key features of the underside of the relay through image processing techniques, and combines convex hull and centroid estimation to construct a three-dimensional reference plane for the gap, which could achieve high-precision, real-time measurement of the gap (with an error less than ±0.0087 mm). The experimental measurement results show that the proposed method is better than the SelfConvNet method, which has a high measurement accuracy and strong anti-interference ability, and an accuracy rate of up to 99.5% in factory relay quality inspection experiments.

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“…However, during image reconstruction, the real-time performance of more complex algorithms is poor and conventional algorithms is difficult to guarantee accuracy. Additionally, the challenges associated with detecting weak capacitance and the soft field characteristics of capacitance measurement [57,58] have yet to be addressed. Hence, this method remains in the progress of laboratory research, which cannot be applied in practical engineering.…”

Section: Electrical Capacitance Tomographymentioning

confidence: 99%

Status Quo and Prospect of Oil-Water Two-phase Flow Detection Technology in Petrochemical Pipeline Network

Shaolin

2023

PPS

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In petrochemical production, the flow of oil-water mixture in pipelines is a common phenomenon referred to as oil-water two-phase flow. The main purpose of oil-water two-phase flow detection is to detect the proportion of oil or water in the liquid to be tested, which is significant for crude oil extraction, storage and transportation and even sales. This article analyzes the impact of complex environments on the detection results of oil-water two-phase flow and introduces the basic principle of oil or water content detection methods in oil-water two-phase flow detection as well as the applicability, advantages and disadvantages of various detection methods. Then the domestic and foreign scholars' efforts toward the fundamentals are briefly introduced. Finally, existing methods are summarized, together with research trends for oil-water two-phase flow.

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Capacitive Sensors for Multiphase Flow Measurement: A Review

Cited by 16 publications

References 154 publications

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors

Gap measurement method based on projection lines and convex analysis of 3D points cloud

Status Quo and Prospect of Oil-Water Two-phase Flow Detection Technology in Petrochemical Pipeline Network

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