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

Abstract

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.

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

“…The methods used in this research can also be applied in various fields of science, including medicine [33,34]. [9] Frequency…”

“…Further, the amplitudes of the first to the fourth dominant frequency of the signals, which are characteristics necessary for neural network training, are extracted. These features have been used as practical features in some other articles [9,30], and we have benefited from this method by being inspired by these articles. MATLAB software has been used to extract Energies 2022, 15, 4500 6 of 12 these features.…”

“…articles [9,30], and we have benefited from this method by being inspired by these articles. MATLAB software has been used to extract these features.…”

“…Although the accuracy of their proposed system was appropriate, non-consideration of scale deposited inside the pipe has been noted from research gaps. In studies [8][9][10], X-rays have been used to determine the parameters of two-phase and three-phase flows. Although characteristic extraction techniques have been used in this research, the use of an X-ray source, despite all its advantages such as safety and the ability to turn on and off, etc., did not allow for the achievement of high accuracy.…”

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

“…In the past few decades, various advanced computational approaches, e.g., finite element, numerical linear algebra, statistics, numerical analysis, tensor analysis, and artificial intelligence, have been applied in various fields of study such as chemical engineering [29][30][31][32][33][34][35][36][37], electrical engineering [38][39][40][41][42][43][44][45][46], biomedical engineering [47][48][49][50][51][52][53][54], civil engineering [55][56][57][58], social sciences [59][60][61][62][63][64][65][66][67][68][69], mechanical engineering [70][71][72][73][74][75][76]…”