The Fuzzy Logic Application in Volume Fractions Prediction of the Annular Three-Phase Flows

Karami, Alimohammad; Roshani, Gholam Hossein; Salehizadeh, A.; Nazemi, Ehsan

doi:10.1007/s10921-017-0415-7

Cited by 24 publications

(6 citation statements)

References 31 publications

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“…ANFIS is regarded as a fuzzy inference system implemented in the mold of neural networks . In fact, ANFIS is a hybrid model which provides the capabilities of neural network topology along with fuzzy logic.…”

Section: The Modeling Approach; Principlesmentioning

confidence: 99%

A comparison between radial basis function (RBF) and adaptive neuro‐fuzzy inference system (ANFIS) to model the free convection in an open round cavity

Akbari

Karami

Ashjaee

2018

Heat Trans. Asian Res.

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This study demonstrates the capability of radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model and predict the free convection heat transfer in an open round cavity. In fact, the effects of the Rayleigh number (Ra) and ratio of the nonconductor barrier distance from the bottom of the cavity to the cavity diameter (H/D), on the free convection in the cavity, are modeled via the RBF and ANFIS models. To start modeling, sufficient data are gathered. Here, data are experimentally generated using a Mach-Zehnder interferometer. In the next step, the RBF and ANFIS models are trained. According to the results, there is an optimum ratio (H/D), in which the heat transfer is maximum. This maximum value increases 2 AKBARI ET AL. 870 agreement with similar ones obtained experimentally. The mean relative errors of the training, testing, and checking data for the RBF model were found as 0.1348%, 1.1972%, and 2.4967%, respectively. Moreover, for the ANFIS model, the error values were 0.0731%, 0.9110%, and 1.9144%, which shows that RBF and ANFIS can predict the results precisely. K E Y W O R D Sadaptive neuro-fuzzy inference system (ANFIS), free convection, Mach-Zender interferometer, Nusselt number (Nu), radial basis function (RBF), round cavity

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Section: The Modeling Approach; Principlesmentioning

confidence: 99%

A comparison between radial basis function (RBF) and adaptive neuro‐fuzzy inference system (ANFIS) to model the free convection in an open round cavity

Akbari

Karami

Ashjaee

2018

Heat Trans. Asian Res.

Self Cite

View full text Add to dashboard Cite

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“…In addition, in a similar study [5], an attempt was made to predict the scale thickness in the pipe independent of the flow regime and volume percentage using the RBF neural network with an RMSE of less than 0.22. Radiation multiphase flowmeters and the applications of artificial intelligence in them can be found in other studies that are available in references [6][7][8][9][10][11][12][13][14][15]. Oil and gas pipelines may become internally fouled and have devastating effects.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.

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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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The Fuzzy Logic Application in Volume Fractions Prediction of the Annular Three-Phase Flows

Cited by 24 publications

References 31 publications

A comparison between radial basis function (RBF) and adaptive neuro‐fuzzy inference system (ANFIS) to model the free convection in an open round cavity

A comparison between radial basis function (RBF) and adaptive neuro‐fuzzy inference system (ANFIS) to model the free convection in an open round cavity

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

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

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