Volume fraction determination of the annular three-phase flow of gas-oil-water using adaptive neuro-fuzzy inference system

被引：17
|
作者：
Roshani, Gholam Hossein [1 ]
Karami, Alimohammad [2 ]
Nazemi, Ehsan [3 ]
Shama, Farzin [4 ]
机构：
[1] Kermanshah Univ Technol, Dept Elect Engn, Kermanshah, Iran
[2] Islamic Azad Univ, Kermanshah Branch, Young Researchers & Elite Club, Kermanshah, Iran
[3] Nucl Sci & Technol Res Inst, Tehran, Iran
[4] Islamic Azad Univ, Kermanshah Branch, Dept Elect Engn, Kermanshah, Iran
来源：
COMPUTATIONAL & APPLIED MATHEMATICS | 2018年 / 37卷 / 04期
关键词：
Annular; Adaptive neuro-fuzzy inference system; Three-phase; GAMMA-RAY ATTENUATION; CONVECTION HEAT-TRANSFER; RADIAL BASIS FUNCTION; LIQUID-PHASE DENSITY; V-SHAPED PLATE; 2-PHASE FLOWS; VOID FRACTION; ISOTHERMAL CYLINDERS; STRATIFIED REGIME; ONE DETECTOR;
D O I：
10.1007/s40314-018-0578-6
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The use of adaptive neuro-fuzzy inference system (ANFIS) has been reported for predicting the volume fractions in a gas-oil-water multiphase system. In fact, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system consisting of and and one NaI detector using ANFIS. Since the summation of volume fractions is constant, therefore ANFIS must predict only two volume fractions. In this study, three ANFIS networks are applied. The first is utilized to predict the gas and water volume fractions. The next one is applied to predict the gas and oil, and the last one is used to predict the water and oil volume fractions. In the next step, ANFIS networks must be trained based on numerically obtained data from MCNP-X code. Then, the average testing errors of these three networks are computed and compared. The network with the least error has been selected as the best predictor model.
引用
收藏
页码：4321 / 4341
页数：21
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