X-ray tube with artificial neural network model as a promising alternative for radioisotope source in radiation based two phase flowmeters

Roshani, Mohammadmehdi; Ali, Peshawa J. Muhammad; Roshani, Gholam Hossein; Nazemi, Behrooz; Corniani, Enrico; Phan, Nhut-Huan; Tran, Hoai-Nam; Nazemi, Ehsan

doi:10.1016/j.apradiso.2020.109255

Cited by 25 publications

(5 citation statements)

References 33 publications

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“…To increase productivity and increase speed in neural network training, available data should be optimized. There are various optimization methods that can be used according to the type and nature of available data [ 49 , 50 , 51 , 52 , 53 ]. In this research, in order to optimize the data, all the input and output data were transferred to a range between 0 and 1, and after training the network, the output data were returned to their original state.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks

et al. 2022

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Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

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Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks

et al. 2022

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“…Alamoudi et al attempted to develop a gamma attenuation technique using RBFNN to determine the scale thickness of oil pipelines where two-phase flow with various symmetric patterns and volume fractions exist [5]. The applications of artificial intelligence in multiphase flowmeters have been discussed in many studies to date, some of which can be found in [6][7][8][9][10][11][12][13][14][15]. In pipelines, valves, and pumps used in the production and processing of oil, scales may form over time.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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).

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“…Advantages such as the ability to adjust the emitted photons energy of the X-ray tube, high-intensity photons emission, the ability to turn it on and off-this advantage is very important for the health of people working with these devices-, and the ability to transport it easily and steadily. Its energy retention over time has led current researchers to use X-ray tubes instead of radioisotopes [15][16][17][18]. In [15], an X-ray densitometry system was introduced.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, an X-ray tube (50 kV, 1 mA) and some linear detectors were applied for two-phase flow metering. In the research [16], an X-ray tube and one NaI detector were used. The annular and stratified flow patterns were simulated.…”

Section: Introductionmentioning

confidence: 99%

Frequency Domain Feature Extraction Investigation to Increase the Accuracy of an Intelligent Nondestructive System for Volume Fraction and Regime Determination of Gas-Water-Oil Three-Phase Flows

et al. 2021

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In this research, a methodology consisting of an X-ray tube, one Pyrex-glass pipe, and two NaI detectors was investigated to determine the type of flow regimes and volume fractions of gas-oil-water three-phase flows. Three prevalent flow patterns—namely annular, stratified, and homogenous—in various volume percentages—10% to 80% with the step of 10%—were simulated by MCNP-X code. After simulating all the states and collecting the signals, the Fast Fourier Transform (FFT) was used to convert the data to the frequency domain. The first and second dominant frequency amplitudes were extracted to be used as the inputs of neural networks. Three Radial Basis Function Neural Networks (RBFNN) were trained for determining the type of flow regimes and predicting gas and water volume fractions. The correct detection of all flow regimes and the determination of volume percentages with a Mean Relative Error (MRE) of less than 2.02% shows that the use of frequency characteristics in determining these important parameters can be very effective. Although X-ray radiation-based two-phase flowmeters have a lot of advantages over the radioisotope-based ones, they suffer from lower measurement accuracy. One reason might be that the X-ray multi-energy spectrum recorded in the detector has been analyzed in a simple way. It is worth mentioning that the X-ray sources generate multi-energy photons despite radioisotopes that generate single energy photons, therefore data analyzing of radioisotope sources would be easier than X-ray ones. As mentioned, one of the problems researchers have encountered is the lower measurement accuracy of the X-ray, radiation-based three-phase flowmeters. The aim of the present work is to resolve this problem by improving the precision of the X-ray, radiation-based three-phase flowmeter using artificial neural network (ANN) and feature extraction techniques.

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X-ray tube with artificial neural network model as a promising alternative for radioisotope source in radiation based two phase flowmeters

Cited by 25 publications

References 33 publications

Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks

Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks

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

Frequency Domain Feature Extraction Investigation to Increase the Accuracy of an Intelligent Nondestructive System for Volume Fraction and Regime Determination of Gas-Water-Oil Three-Phase Flows

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