An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

Roshani, Gholam Hossein; Karami, Ali; Nazemi, Ehsan

doi:10.1007/s40314-019-0772-1

Cited by 21 publications

(14 citation statements)

References 57 publications

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“…In recent years, researchers have used mathematical models called artificial neural networks to help them understand how radiation interacts with tissue [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Moreover, the strong mathematical tool of numerical computing [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ] has been employed to solve various engineering challenges, most notably in the field of artificial networks [ 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

Zhang

Wang

et al. 2023

Diagnostics

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The air kerma, which is the amount of energy given off by a radioactive substance, is essential for medical specialists who use radiation to diagnose cancer problems. The amount of energy that a photon has when it hits something can be described as the air kerma (the amount of energy that was deposited in the air when the photon passed through it). Radiation beam intensity is represented by this value. Hospital X-ray equipment has to account for the heel effect, which means that the borders of the picture obtain a lesser radiation dosage than the center, and that air kerma is not symmetrical. The voltage of the X-ray machine can also affect the uniformity of the radiation. This work presents a model-based approach to predict air kerma at various locations inside the radiation field of medical imaging instruments, making use of just a small number of measurements. Group Method of Data Handling (GMDH) neural networks are suggested for this purpose. Firstly, a medical X-ray tube was modeled using Monte Carlo N Particle (MCNP) code simulation algorithm. X-ray tubes and detectors make up medical X-ray CT imaging systems. An X-ray tube’s electron filament, thin wire, and metal target produce a picture of the electrons’ target. A small rectangular electron source modeled electron filaments. An electron source target was a thin, 19,290 kg/m3 tungsten cube in a tubular hoover chamber. The electron source–object axis of the simulation object is 20° from the vertical. For most medical X-ray imaging applications, the kerma of the air was calculated at a variety of discrete locations within the conical X-ray beam, providing an accurate data set for network training. Various locations were taken into account in the aforementioned voltages inside the radiation field as the input of the GMDH network. For diagnostic radiology applications, the trained GMDH model could determine the air kerma at any location in the X-ray field of view and for a wide range of X-ray tube voltages with a Mean Relative Error (MRE) of less than 0.25%. This study yielded the following results: (1) The heel effect is included when calculating air kerma. (2) Computing the air kerma using an artificial neural network trained with minimal data. (3) An artificial neural network quickly and reliably calculated air kerma. (4) Figuring out the air kerma for the operating voltage of medical tubes. The high accuracy of the trained neural network in determining air kerma guarantees the usability of the presented method in operational conditions.

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Section: Methodsmentioning

confidence: 99%

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

Zhang

Wang

et al. 2023

Diagnostics

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“…Therefore, the implementation of a control system to determine the amount and type of product inside the pipe is very important. Previous researchers have used gamma-ray-based systems as a reliable system for determining the parameters of two-phase [ 1 , 2 , 3 ] and three-phase [ 4 , 5 , 6 ] flows. In [ 1 ], researchers used wavelet transform as an effective method for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, an adaptive neuro-fuzzy inference system (ANFIS) has been used to predict volume percentages, which obtained relatively good accuracy for predicting volume percentages. Studies [ 5 , 6 ] examined the performance of a GMDH neural network and Jaya algorithm to determine volume percentages of three-phase flows, respectively. In Articles [ 7 , 8 ], Sattari et al used time-domain feature extraction techniques to increase the accuracy of determining the type of flow regimes and volume percentages, and they implemented neural networks such as GMDH and MLP with the time characteristics.…”

Section: Introductionmentioning

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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“…The formation of scales makes the internal cross-section of the pipe smaller and reduces the fluid flow, and failure to identify this problem causes malfunctions in pumps and related equipment, emergency shutdowns, damage to oil equipment, increased repair costs, and reduced efficiency. Researchers who considered it necessary to have accurate detection systems to detect the amount of scale inside the pipe always used gamma ray attenuation systems as the gold standard in determining the various parameters of multiphase flows [1][2][3][4][5][6][7][8]. In the study [1], an attempt was made to predict the volume percentage and classification of flow regimes.…”

Section: Introductionmentioning

confidence: 99%

“…In 2016, in similar research, a 60 Co source and a NaI detector were used for obtaining the kind of flow patterns and volume percentage, which was met with low accuracy and unfavorable results due to the lack of extraction of appropriate characteristics from the received signals [3]. In 2019, authors used Jaya's optimization algorithm for predicting the volume percentage of a three-phase flow in the stratified pattern [4]. Sattari et al in [5] proposed a structure with a cesium source and two NaI detectors around the tested pipe to be able to perform volume percentages prediction and classification of flow regimes with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Increasing the Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform

Mayet

Chen

Alizadeh³

et al. 2022

Separations

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Loss of energy, decrement of efficiency, and decrement of the effective diameter of the oil pipe are among the consequences of scale inside oil condensate transfer pipes. To prevent these incidents and their consequences and take timely action, it is important to detect the amount of scale. One of the accurate diagnosis methods is the use of non-invasive systems based on gamma-ray attenuation. The detection method proposed in this research consists of a detector that receives the radiation sent by the gamma source with dual energy (radioisotopes 241Am and 133Ba) after passing through the test pipe with inner scale (in different thicknesses). This structure was simulated by Monte Carlo N Particle code. The simulation performed in the test pipe included a three-phase flow consisting of water, gas, and oil in a stratified flow regime in different volume percentages. The signals received by the detector were processed by wavelet transform, which provided sufficient inputs to design the radial basis function (RBF) neural network. The scale thickness value deposited in the pipe can be predicted with an MSE of 0.02. The use of a detector optimizes the structure, and its high accuracy guarantees the usefulness of its use in practical situations.

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An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

Cited by 21 publications

References 57 publications

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

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 Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform

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