Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries

Hosseini, Siavash; Taylan, Osman; Abusurrah, Mona; Akilan, Thangarajah; Nazemi, Ehsan; Eftekhari-Zadeh, Ehsan; Bano, Farheen; Roshani, Gholam Hossein

doi:10.3390/polym13213647

Cited by 43 publications

(26 citation statements)

References 89 publications

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“…For example, in [4], the best features are selected from an innovative method by measuring the distance between different classes. In papers [7,10], from the signals of approximation and detail extracted by wavelet transform, another step of time characteristic extraction is performed. In study [28], using correlation analysis, the characteristics that have the least similarity with other characteristics were selected as effective characteristics.…”

Section: Signal Processingmentioning

confidence: 99%

“…Failure to use characteristic extraction techniques in these studies prevented access to high accuracy. Researchers examined a large number of time characteristics [4,5], frequency characteristics [6], and wavelet features [7] to determine the type of flow regimes and volume percentages. Although the accuracy of their proposed system was appropriate, non-consideration of scale deposited inside the pipe has been noted from research gaps.…”

Section: Introductionmentioning

confidence: 99%

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Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

et al. 2022

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In this paper, gamma attenuation has been utilised as a veritable tool for non-invasive estimation of the thickness of scale deposits. By simulating flow regimes at six volume percentages and seven scale thicknesses of a two phase-flow in a pipe, our study utilised a dual-energy gamma source with Ba-133 and Cs-137 radioisotopes, a steel pipe, and a 2.54 cm × 2.54 cm sodium iodide (NaI) photon detector to analyse three different flow regimes. We employed Fourier transform and frequency characteristics (specifically, the amplitudes of the first to fourth dominant frequencies) to transform the received signals to the frequency domain, and subsequently to extract the various features of the signal. These features were then used as inputs for the group method for data Hiding (GMDH) neural network framework used to predict the scale thickness inside the pipe. Due to the use of appropriate features, our proposed technique recorded an average root mean square error (RMSE) of 0.22, which is a very good error compared to the detection systems presented in previous studies. Moreover, this performance is indicative of the utility of our GMDH neural network extraction process and its potential applications in determining parameters such as type of flow regime, volume percentage, etc. in multiphase flows and across other areas of the oil and gas industry.

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Section: Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

et al. 2022

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“…Gasoil and air were considered as the liquid and gas phases, respectively. A 137 Cs source and two NaI detectors In recent years, many researchers have put a great deal of effort into oil and gas fields for flow regime identification and void fraction measurement by utilizing different methods such as GMDH and wavelet feature extraction [16][17][18].…”

Section: Simulation Proceduresmentioning

confidence: 99%

“…Different features in the frequency domain were presented by Hanus and co-workers in order to identify the flow regimes in a dynamic condition [10]. In recent years, many researchers have put a great deal of effort into oil and gas fields for flow regime identification and void fraction measurement by utilizing different methods such as GMDH and wavelet feature extraction [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Accurate Flow Regime Classification and Void Fraction Measurement in Two-Phase Flowmeters Using Frequency-Domain Feature Extraction and Neural Networks

et al. 2022

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Two-phase flow is very important in many areas of science, engineering, and industry. Two-phase flow comprising gas and liquid phases is a common occurrence in oil and gas related industries. This study considers three flow regimes, including homogeneous, annular, and stratified regimes ranging from 5–90% of void fractions simulated via the Mont Carlo N-Particle (MCNP) Code. In the proposed model, two NaI detectors were used for recording the emitted photons of a cesium 137 source that pass through the pipe. Following that, fast Fourier transform (FFT), which aims to transfer recorded signals to frequency domain, was adopted. By analyzing signals in the frequency domain, it is possible to extract some hidden features that are not visible in the time domain analysis. Four distinctive features of registered signals, including average value, the amplitude of dominant frequency, standard deviation (STD), and skewness were extracted. These features were compared to each other to determine the best feature that can offer the best separation. Furthermore, artificial neural network (ANN) was utilized to increase the efficiency of two-phase flowmeters. Additionally, two multi-layer perceptron (MLP) neural networks were adopted for classifying the considered regimes and estimating the volumetric percentages. Applying the proposed model, the outlined flow regimes were accurately classified, resulting in volumetric percentages with a low root mean square error (RMSE) of 1.1%.

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“…Researchers have claimed that the feature extraction techniques can help increase the accuracy of determining the type of flow regimes. In their study, Hosseini et al [26] simulated a laboratory structure with an MCNP transport code in which they implemented three homogeneous, stratified, and annular regimes in different volume percentages. A cesium-137 source and a NaI detector were used in this simulation.…”

Section: Introductionmentioning

confidence: 99%

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

et al. 2021

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This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

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Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries

Cited by 43 publications

References 89 publications

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

Accurate Flow Regime Classification and Void Fraction Measurement in Two-Phase Flowmeters Using Frequency-Domain Feature Extraction and Neural Networks

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

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