Online measuring density of oil products in annular regime of gas-liquid two phase flows

被引：72

作者：

Roshani, Gholam Hossein ^{[1
]}

Roshani, Sobhan ^{[2
]}

Nazemi, Ehsan ^{[3
]}

Roshani, Saeed ^{[2
]}

机构：

[1] Kermanshah Univ Technol, Elect Engn Dept, Kermanshah, Iran

[2] Islamic Azad Univ, Dept Elect Engn, Kermanshah Branch, Kermanshah, Iran

[3] Nucl Sci & Technol Res Inst, Tehran, Iran

来源：

MEASUREMENT | 2018年 / 129卷

关键词：

Density; Oil products; Gamma ray; Nuclear gauges; Artificial neural network; GAMMA-RAY ATTENUATION; ARTIFICIAL NEURAL-NETWORK; VOLUME FRACTION PREDICTION; VOID FRACTION; PETROLEUM-PRODUCTS; MONITORING APPLICATIONS; MULTIPHASE FLOWS; PIPE-FLOW; DENSITOMETER;

D O I：

10.1016/j.measurement.2018.07.026

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Gamma-ray densitometry is widely implemented in oil industry because it is an online technique and also has a good precision. If there is single phase flow in oil pipelines, measuring the density is possible just by using one source and one detector. But if in addition to oil, there is gas in oil pipelines and in fact there is a two-phase flow, conventional gamma ray densitometry (one source and one detector) could not be used for determining the density of liquid phase. In this study, a novel method is proposed for online measuring density of liquid phase in annular regime of liquid-gas two-phase flows using dual modality densitometry technique and artificial neural network (ANN). An experimental setup was designed in order to provide the required input data for training and testing the network. Registered counts in both scattering and transmission detectors were used as the inputs of the ANN and density of liquid phase was used as the output of the ANN. Using the proposed methodology, density of liquid phase was predicted with error of less than 0.031 g/cm(-3) in annular regime of gas-liquid two phase flows for void fractions in the range of 10-70 percentages.

引用

页码：296 / 301

页数：6

共 25 条

[1]

Åbro E, 1999, MEAS SCI TECHNOL, V10, P619, DOI 10.1088/0957-0233/10/7/308

[2] Measurements of the density of petroleum products and their mixtures by the ultrasonic method [J].

Chakhlov, VL ;

Cheprasov, AI ;

Shaverin, NV .

RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING, 2002, 38 (06) :472-476

[3] Analysis of CHF in saturated forced convective boiling on a heated surface with impinging jets using artificial neural network and genetic algorithm [J].

Cong, Tenglong ;

Chen, Ronghua ;

Su, Guanghui ;

Qiu, Suizheng ;

Tian, Wenxi .

NUCLEAR ENGINEERING AND DESIGN, 2011, 241 (09) :3945-3951

[4] A WOOD DENSITOMETER USING DIRECT SCANNING WITH X-RAYS [J].

COWN, DJ ;

CLEMENT, BC .

WOOD SCIENCE AND TECHNOLOGY, 1983, 17 (02) :91-99

[5] Intercomparison of gamma ray scattering and transmission techniques for gas volume fraction measurements in two phase pipe flow [J].

El Abd, A. .

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2014, 735 :260-266

[6] ON LEARNING THE DERIVATIVES OF AN UNKNOWN MAPPING WITH MULTILAYER FEEDFORWARD NETWORKS [J].

GALLANT, AR ;

WHITE, H .

NEURAL NETWORKS, 1992, 5 (01) :129-138

[7]

Gol'din M. L., 1961, ATOM ENERGY J, V9, P753, DOI [10.1007/BF01709207, DOI 10.1007/BF01709207]

[8]

Jing CG, 2006, LECT NOTES COMPUT SC, V3973, P1248

[9] Flow Regime Identification of Gas/Liquid Two-phase Flow in Vertical Pipe Using RBF Neural Networks [J].

Jing Chunguo ;

Bai Qiuguo .

CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, :5143-5147

[10] Developing a gamma ray fluid densitometer in petroleum products monitoring applications using Artificial Neural Network [J].

Khorsandi, M. ;

Feghhi, S. A. H. ;

Salehizadeh, A. ;

Roshani, G. H. .

RADIATION MEASUREMENTS, 2013, 59 :183-187

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