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3. An optimised and novel capacitance-based sensor design for measuring void fraction in gas–oil two-phase flow systems.

Author

Iliyasu, Abdullah M., Shahsavari, Mohammad Hossein, Benselama, Abdullah S., Nazemi, Ehsan, and Salama, Ahmed S.

Subjects
STRATIFIED flow, ARTIFICIAL intelligence, POROSITY, TWO-phase flow, MULTIPHASE flow
Abstract

This study explores a new electrode configuration for measuring the void fraction of two-phase flows using capacitance-based sensors. The proposed method is considered 'skewed' because of its unique geometric shape, and the performance of the proposed sensor was evaluated and improved via multiple simulations using the COMSOL Multiphysics software. The simulations encompass three different flow patterns, stratified, annular and homogeneous, whose performance themselves were verified in a previous study. The influences of geometric properties and parameters on the sensitivity of the proposed sensor were evaluated to determine an optimal configuration. Furthermore, the sensitivity distribution on different void fractions of the sensor was analysed for various void fractions in different flow patterns. Additionally, the proposed configuration was also compared alongside double-ring and concave sensors to determine the overall sensitivity. At 2.11 pF, the overall sensitivity of the proposed sensor was significantly higher than that of the other sensors. It is worth mentioning that the measurement precision of multiphase flow meters is of high importance, particularly in petroleum industry because of the oil price and also the high amount of transported products. HIGHLIGHTS: This study explores a new electrode configuration for measuring void fraction of two-phase flows using capacitance-based sensors The COMSOL simulations encompass three different flow patterns: stratified, annular and homogeneous The influences of geometric properties and parameters on the sensitivity of the proposed sensor were evaluated to determine an optimal configuration Sensitivity distribution of the sensor was analysed for various void fractions in different flow patterns The proposed configuration was also compared alongside double-ring and concave sensors to determine the overall sensitivity At 2.11 pF, the overall sensitivity of the proposed sensor was significantly higher than that of the other sensors [ABSTRACT FROM AUTHOR]

Published
2024
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9. Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors.

Author

Mayet, Abdulilah Mohammad, Fouladinia, Farhad, Alizadeh, Seyed Mehdi, Alhashim, Hala H., Guerrero, John William Grimaldo, Loukil, Hassen, Parayangat, Muneer, Nazemi, Ehsan, and Shukla, Neeraj Kumar

Subjects
CAPACITIVE sensors, ARTIFICIAL neural networks, FLOW separation, ARTIFICIAL intelligence, POROSITY, NANOFLUIDICS
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow's characteristics independent of the pipe's scale layer

Author

Mayet, Abdulilah Mohammad, Salama, Ahmed S., Alizadeh, Seyed Mehdi, Nesic, Slavko, Guerrero, John William Grimaldo, Eftekhari-Zadeh, Ehsan, Nazemi, Ehsan, and Iliyasu, Abdullah M.

Subjects
Computer. Automation, Physics
Abstract

Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presence of scale inside the test pipe. In this non-invasive method, a dual-energy source of barium-133 and cesium-137 isotopes is irradiated, and the photons are absorbed by a detector as they pass through the test pipe on the other side of the pipe. The Monte Carlo N Particle Code (MCNP) simulates the structure and frequency features, such as the amplitudes of the first, second, third, and fourth dominant frequencies, which are extracted from the data recorded by the detector. These features use radial basis function neural network (RBFNN) inputs, where two neural networks are also trained to accurately determine the volume percentage and correctly classify all flow patterns, independent of scale thickness in the pipe. The advantage of the proposed system in this study compared to the conventional systems is that it has a better measuring precision as well as a simpler structure (using one detector instead of two).

Published
2022

29. Central nervous system : overall considerations based on hardware realization of digital spiking silicon neurons (DSSNs) and synaptic coupling

Author

Balubaid, Mohammed, Taylan, Osman, Yilmaz, Mustafa Tahsin, Eftekhari-Zadeh, Ehsan, Nazemi, Ehsan, and Alamoudi, Mohammed

Subjects
Mathematics
Abstract

The Central Nervous System (CNS) is the part of the nervous system including the brain and spinal cord. The CNS is so named because the brain integrates the received information and influences the activity of different sections of the bodies. The basic elements of this important organ are: neurons, synapses, and glias. Neuronal modeling approach and hardware realization design for the nervous system of the brain is an important issue in the case of reproducing the same biological neuronal behaviors. This work applies a quadratic-based modeling called Digital Spiking Silicon Neuron (DSSN) to propose a modified version of the neuronal model which is capable of imitating the basic behaviors of the original model. The proposed neuron is modeled based on the primary hyperbolic functions, which can be realized in high correlation state with the main model (original one). Really, if the high-cost terms of the original model, and its functions were removed, a low-error and high-performance (in case of frequency and speed-up) new model will be extracted compared to the original model. For testing and validating the new model in hardware state, Xilinx Spartan-3 FPGA board has been considered and used. Hardware results show the high-degree of similarity between the original and proposed models (in terms of neuronal behaviors) and also higher frequency and low-cost condition have been achieved. The implementation results show that the overall saving is more than other papers and also the original model. Moreover, frequency of the proposed neuronal model is about 168 MHz, which is significantly higher than the original model frequency, 63 MHz.

Published
2022

32. Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

Author

Alanazi, Abdullah K., primary, Alizadeh, Seyed Mehdi, additional, Nurgalieva, Karina Shamilyevna, additional, Nesic, Slavko, additional, Grimaldo Guerrero, John William, additional, Abo-Dief, Hala M., additional, Eftekhari-Zadeh, Ehsan, additional, Nazemi, Ehsan, additional, and Narozhnyy, Igor M., additional

Published
2022
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41. Feasibility study of using X-ray tube and GMDH for measuring volume fractions of annular and stratified regimes in three-phase flows

Author

Roshani, Gholam Hossein, Ali, Peshawa Jammal Muhammad, Mohammed, Shivan, Hanus, Robert, Abdulkareem, Lokman, Alanezi, Adnan Alhathal, Nazemi, Ehsan, Eftekhari-Zadeh, Ehsan, and Kalmoun, El Mostafa

Subjects
X-ray tube, volume fraction, lcsh:Mathematics, photon, stratified regime, artificial intelligence, lcsh:QA1-939, Engineering sciences. Technology, annular regime
Abstract

In this paper, the feasibility of using an X-ray tube instead of radioisotope sources for measuring volume fractions of gas, oil, and water in two typical flow regimes of three-phase flows, namely, annular and stratified, is evaluated. This study's proposed detection system is composed of an X-ray tube, a 1 inch x 1 inch NaI detector, and one Pyrex-glass pipe to model different volume fractions for two flow regimes, annular and stratified. Group method of data handling (GMDH), a powerful regression tool, was also implemented to analyze the obtained data. The obtained results in this work indicate that a simple system based on an X-ray tube and just one NaI detector could be a potential alternative to radioisotope-based systems for separate measurements of gas, oil, and water volume fractions in annular and stratified flow regimes of a three-phase flow.

Published
2021

44. Proposing a Nondestructive and Intelligent System for Simultaneous Determining Flow Regime and Void Fraction Percentage of Gas–Liquid Two Phase Flows Using Polychromatic X-Ray Transmission Spectra

Author

Amiri, Saba, primary, Ali, Peshawa Jammal Muhammad, additional, Mohammed, Shivan, additional, Hanus, Robert, additional, Abdulkareem, Lokman, additional, Alanezi, Adnan Alhathal, additional, Eftekhari-Zadeh, Ehsan, additional, Roshani, Gholam Hossein, additional, Nazemi, Ehsan, additional, and Kalmoun, El Mostafa, additional

Published
2021
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45. Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products

Author

Roshani, Gholam Hossein, primary, Ali, Peshawa Jammal Muhammad, additional, Mohammed, Shivan, additional, Hanus, Robert, additional, Abdulkareem, Lokman, additional, Alanezi, Adnan Alhathal, additional, Sattari, Mohammad Amir, additional, Amiri, Saba, additional, Nazemi, Ehsan, additional, Eftekhari-Zadeh, Ehsan, additional, and Kalmoun, El Mostafa, additional

Published
2021
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50. Designing a simple radiometric system to predict void fraction percentage independent of flow pattern using radial basis function

Author

Nazemi, Ehsan, Roshani, Gholam H., Shama, Farzin, Imani, Mohammad A., and Mohammadi, Salar

Subjects
Physics
Abstract

The void fraction is one of the most important parameters characterizing a multiphase flow. The prediction of the performance of any system operating with more than single phase relies on our knowledge and ability to measure the void fraction. In this work, a validated simulation study was performed in order to predict the void fraction independent of the flow pattern in gas-liquid two-phase flows using a gamma ray Co-60 source and just one scintillation detector with the help of an artificial neural network (ANN) model of radial basis function (RBF). Three used inputs of ANN include a registered count under Compton continuum and counts under full energy peaks of 1173 and 1333 keV. The output is a void fraction percentage. Applying this methodology, the percentage of void fraction independent of the flow pattern of a gas-liquid two-phase flow was estimated with a mean relative error less than 1.17%. Although the error obtained in this study is almost close to those obtained in other similar works, only one detector was used, while in the previous studies at least two detectors were employed. Advantages of using fewer detectors are: cost reduction and system simplification.

Published
2018

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