scholarly journals 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

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2091
Author(s):  
Osman Taylan ◽  
Mohammad Amir Sattari ◽  
Imene Elhachfi Essoussi ◽  
Ehsan Nazemi
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Frequency Domain ◽  
Measurement Accuracy ◽  
Flow Regimes ◽  
Water Volume ◽  
X Ray ◽  
Volume Fractions ◽  
Three Phase

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.

scholarly journals Feasibility Study of Using X-ray Tube and GMDH for Measuring Volume Fractions of Annular and Stratified Regimes in Three-Phase Flows

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 613
Author(s):  
Gholam Hossein Roshani ◽  
Peshawa Jammal Muhammad Ali ◽  
Shivan Mohammed ◽  
Robert Hanus ◽  
Lokman Abdulkareem ◽  
...  
Keyword(s):  
Detection System ◽  
Flow Regimes ◽  
Water Volume ◽  
Group Method ◽  
Gas Oil ◽  
Measuring Volume ◽  
X Ray ◽  
Volume Fractions ◽  
Three Phase ◽  
Three Phase Flow

In this paper, the feasibility of using an X-ray tube instead of radioisotope sources for measuring volume fractions of gas, oil, and water in two typical flow regimes of three-phase flows, namely, annular and stratified, is evaluated. This study’s proposed detection system is composed of an X-ray tube, a 1 inch × 1 inch NaI detector, and one Pyrex-glass pipe to model different volume fractions for two flow regimes, annular and stratified. Group method of data handling (GMDH), a powerful regression tool, was also implemented to analyze the obtained data. The obtained results in this work indicate that a simple system based on an X-ray tube and just one NaI detector could be a potential alternative to radioisotope-based systems for separate measurements of gas, oil, and water volume fractions in annular and stratified flow regimes of a three-phase flow.

Application of High Temperature Quantitative X-Ray Powder Diffraction on Determination of Ternary System

2011 ◽  
Vol 689 ◽  
pp. 355-360
Author(s):  
Qiu Guo Xiao ◽  
Gang Cheng Ding ◽  
Tang Zhong Long ◽  
Shao Hua Shen
Keyword(s):  
High Temperature ◽  
Isothermal Section ◽  
Liquid Phase ◽  
Ternary System ◽  
Solid Phase ◽  
Phase Region ◽  
Phase Point ◽  
X Ray ◽  
Three Phase

This paper has put forward a high-temperature quantitative X-ray powder diffraction analysis method for the determination of an isothermal section of a ternary system in comparison with a conventional method. In a three-phase region of the isothermal section at 1150 °C of Cu2O(CuO)-Al2O3-SiO2 pseudo-ternary system, the compositions of the solid phase points of three system points are determined according to the quantitative analysis of the crystalline phases in the samples carried out by Rietveld method. Then the liquid phase point of the three-phase region is determined according to the crosspoints of the tie lines of every pair of system point and solid phase point. The precisions of the analytical results have reached to be 0.1 ~ 5.0 %. By comparison, a good result is obtained for the determination of the liquid phase point of the three-phase region in the isothermal section at 1150 °C when the analytical results of high-temperature RQA analysis are used in determination of the isothermal section of the pseudo-ternary system.

scholarly journals Determination of Nutrients in Liquid Manures and Biogas Digestates by Portable Energy-Dispersive X-ray Fluorescence Spectrometry

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3892
Author(s):  
Michael Horf ◽  
Robin Gebbers ◽  
Sebastian Vogel ◽  
Markus Ostermann ◽  
Max-Frederik Piepel ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Organic Fertilizers ◽  
Measuring System ◽  
Reference Analysis ◽  
Element Analysis ◽  
X Ray ◽  
Site Analysis ◽  
Iron Copper ◽  
Potassium Magnesium

Knowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization. Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements. A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation analysis for liquid samples (original and filtered) resulted in lower R2s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.

Element-specific determination of X-ray transmission signatures using neural networks

2009 ◽  
Vol 42 (5) ◽  
pp. 446-451 ◽  
Author(s):  
Charles R. Day ◽  
James C. Austin ◽  
John B. Butcher ◽  
Peter W. Haycock ◽  
Anthony T. Kearon
Keyword(s):  
Neural Networks ◽  
X Ray ◽  
Specific Determination

scholarly journals Abstract P-49: Determination of Relative Positions of Two Helices in Two-Helical Proteins Basing on X-ray Diffraction Data with the Use of Convolutional Neural Networks

2019 ◽  
Vol 9 (Suppl_1) ◽  
pp. S39-S39
Author(s):  
Valery Novoseletsky ◽  
Mikhail Lozhnikov ◽  
Grigoriy Armeev ◽  
Alexandr Kudriavtsev ◽  
Alexey Shaytan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Diffraction Data ◽  
X Ray Diffraction ◽  
X Ray ◽  
Helical Proteins
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