Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network

Salgado, César Marques; Brandão, Luís Eduardo Barreira; Schirru, Roberto; Pereira, Cláudio M.N.A.; Silva, Ademir Xavier da; Ramos, Robson

doi:10.1016/j.apradiso.2009.02.093

Cited by 100 publications

(9 citation statements)

References 13 publications

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“…The MLP model consists of two crucial subsets of data: the train set and the test set. The train set comprises carefully curated data points used to train the model’s cognitive abilities, while the test set contains unfamiliar data to evaluate the network’s effectiveness and accuracy [ 42 ]. To achieve an optimal configuration for the ANN with low Mean Absolute Error (MAE), a comprehensive array of structures was thoroughly assessed, oscillating between a spectrum of framework properties, encompassing epoch quantities, hidden layers, and activation functions.…”

Section: Development Of the Predicting Systemmentioning

confidence: 99%

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: Development Of the Predicting Systemmentioning

confidence: 99%

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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show abstract

“…Generally, a combination of sensors is required to determine shares of each phase in a multiphase flow. The sensors required for measuring the share of each phase in a multiphase flow include gamma-ray [17][18][19], capacitance and conductance [20], microwave [21], and near-infrared probes [8,9]. One of the emerging technologies for measuring multiphase flow rates is soft computing and virtual metering [22,23].…”

Section: Introductionmentioning

confidence: 99%

Deep learning models for improved accuracy of a multiphase flowmeter

Manami

Seddighi

2023

Measurement

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“…The type of flow pattern impacts the efficiency of the separation process in such a way that the percentage of each component indicates whether the drilling needs to be stopped or not. The mixture of gamma radiation and ANNs have contributed in a lot of researches as a practical tool [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…In [1], a calculation of volumetric percentages in three-phase flows was performed by using a dual-energy source and three detectors. Simulations were performed by MCNP-4C code.…”

Section: Introductionmentioning

confidence: 99%

Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

et al. 2021

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The increasing consumption of fossil fuel resources in the world has placed emphasis on flow measurements in the oil industry. This has generated a growing niche in the flowmeter industry. In this regard, in this study, an artificial neural network (ANN) and various feature extractions have been utilized to enhance the precision of X-ray radiation-based two-phase flowmeters. The detection system proposed in this article comprises an X-ray tube, a NaI detector to record the photons, and a Pyrex-glass pipe, which is placed between detector and source. To model the mentioned geometry, the Monte Carlo MCNP-X code was utilized. Five features in the time domain were derived from the collected data to be used as the neural network input. Multi-Layer Perceptron (MLP) was applied to approximate the function related to the input-output relationship. Finally, the introduced approach was able to correctly recognize the flow pattern and predict the volume fraction of two-phase flow’s components with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of less than 0.51, 0.4 and 1.16%, respectively. The obtained precision of the proposed system in this study is better than those reported in previous works.

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Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network

Cited by 100 publications

References 13 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

Deep learning models for improved accuracy of a multiphase flowmeter

Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

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