Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Improving the performance of gamma radiation based two phase flow meters using optimal time characteristics of the detector output signal extraction

Document Type : Research Paper

Authors

M.A. Sattari 1
N. Korani 1
R. Hanus 2
Gh.H. Roshani 1
E. Nazemi 3

1 Electrical Engineering Department, Kermanshah University of Technology, P.O.Box: 6715685420, Kermanshah - Iran

2 Rzeszow University of Technology, P.O.Box: 35-959, Rzeszow – Poland

3 Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box: 14155-1339, Tehran - Iran

https://doi.org/10.24200/nst.2020.1116
Abstract
Measuring volume fractions and identifying the flow regime are important challenges in the oil industry. In the present study, three different flow regimes were simulated by MCNPX code. A 137Cs source and two NaI detectors have been used in order to count the transmitted photons. The counted data had high-frequency noises. In order to tackle this problem, a Savitzky-Golay filter was applied. Therefore, four features in the time domain including STD, Skewness, Kurtosis, and Maximum Value were extracted. It was found that the extracted features are not capable of separating the flow regimes completely, without overlap. Accordingly, three different features from registered data of both detectors were extracted. After investigating all the possible statues, two ANNs were implemented to identify the flow regimes and predict the void fraction, respectively. By applying this method, all the three flow regimes were correctly distinguished and void fraction was predicted with root mean square error (RMSE) of less than 0.59.

Highlights

1.             G.H. Roshani, E. Nazemi, Calculation of volume fraction in multiphase flow using gamma rays attenuation in the petroleum, gas and petrochemical industry, First International Comprehensive competition Conference on Engineering Sciences in Iran (in Persian).

 

2.             C.M. Bishop, G.D. James, Analysis of multiphase flows using dual-energy gamma densitometry and neural networks, Nuclear Instruments and Methods in Physics Research A., 327, 580-593 (1993).

 

3.             E. Abro, G.A. Johansen, Improved Void Fraction Determination by Means of Multibeam Gamma-Ray Attenuation Measurements, Flow Measurement and Instrumentation, 10(2), 99-108 (1999).

 

4.             E. Nazemi, et al., Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. International Hydrogen Energy, (2016).

 

5.             G.H. Roshani, et al., Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation, Measurement (2014).

 

6.             G.H. Roshani, E. Nazemi, S.A.H. Feghhi, Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas-liquid two-phase flows, Flow Measurement and Instrumentation, 73–79 (2016).

 

7.             R. Hanu, et al., Signals feature extraction in liquid-gas flow measurements using gamma densitometry time domain, EDP Science, (2016).

 

8.             R. Hanus, et al., Identification of liquid-gas flow regime in a pipeline using gamma-ray absorption technique and computational intelligence methods, Flow Measurement and Instrumentation, (2018).

 

9.             M. Hayati, et al., An Optimized Design of Anode Shape Based on Artificial Neural Network for Achieving Highest X-ray Yield in Plasma Focus Device, Journal of fusion energy, 32, 615-621 (2013).

 

10.          M. Khorsandi, et al., Developing a Gamma ray Fluid Densitometer in Petroleum Products using Artificial Neural Network, Radiation Measurement (2013).

 

11.          G.H. Roshani, et al, Application of adaptive neuro-fuzzy inference system in prediction of fluid density for a gamma ray densitometer in petroleum products monitoring, Measurement 46, 3276-3281 (2013).

 

12.          C.M. Salgado, et al, Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks, Progress in Nuclear Energy, 52, 555-562 (2010).

 

13.          C.M. Salgado, et al, Salinity independent volume fraction prediction in annular and stratified (water-gas-oil) multiphase flows using artificial neural networks, Progress in Nuclear Energy, 76, 17-23 (2014).

Keywords

Two-phase flow regimes
Savitzky-Golay filter
Feature extraction
Neural network
1.             G.H. Roshani, E. Nazemi, Calculation of volume fraction in multiphase flow using gamma rays attenuation in the petroleum, gas and petrochemical industry, First International Comprehensive competition Conference on Engineering Sciences in Iran (in Persian).
 
2.             C.M. Bishop, G.D. James, Analysis of multiphase flows using dual-energy gamma densitometry and neural networks, Nuclear Instruments and Methods in Physics Research A., 327, 580-593 (1993).
 
3.             E. Abro, G.A. Johansen, Improved Void Fraction Determination by Means of Multibeam Gamma-Ray Attenuation Measurements, Flow Measurement and Instrumentation, 10(2), 99-108 (1999).
 
4.             E. Nazemi, et al., Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. International Hydrogen Energy, (2016).
 
5.             G.H. Roshani, et al., Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation, Measurement (2014).
 
6.             G.H. Roshani, E. Nazemi, S.A.H. Feghhi, Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas-liquid two-phase flows, Flow Measurement and Instrumentation, 73–79 (2016).
 
7.             R. Hanu, et al., Signals feature extraction in liquid-gas flow measurements using gamma densitometry time domain, EDP Science, (2016).
 
8.             R. Hanus, et al., Identification of liquid-gas flow regime in a pipeline using gamma-ray absorption technique and computational intelligence methods, Flow Measurement and Instrumentation, (2018).
 
9.             M. Hayati, et al., An Optimized Design of Anode Shape Based on Artificial Neural Network for Achieving Highest X-ray Yield in Plasma Focus Device, Journal of fusion energy, 32, 615-621 (2013).
 
10.          M. Khorsandi, et al., Developing a Gamma ray Fluid Densitometer in Petroleum Products using Artificial Neural Network, Radiation Measurement (2013).
 
11.          G.H. Roshani, et al, Application of adaptive neuro-fuzzy inference system in prediction of fluid density for a gamma ray densitometer in petroleum products monitoring, Measurement 46, 3276-3281 (2013).
 
12.          C.M. Salgado, et al, Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks, Progress in Nuclear Energy, 52, 555-562 (2010).
 
13.          C.M. Salgado, et al, Salinity independent volume fraction prediction in annular and stratified (water-gas-oil) multiphase flows using artificial neural networks, Progress in Nuclear Energy, 76, 17-23 (2014).

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