Combination of a gamma radiation-based system and the adaptive network-based fuzzy inference system (ANFIS) for calculating the volume fraction in stratified regime of a three-phase flow
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Understanding the volume fraction of water-oil-gas three-phase flow is of significant importance in oil and gas industry.
The current research attempts to indicate the ability of adaptive network-based fuzzy inference system (ANFIS) to forecast the volume fractions in a water-oil-gas three-phase flow system.
The current investigation devotes to measure the volume fractions in the stratified three-phase flow, on the basis of a dual-energy metering system consisting of the 152Eu and 137Cs and one NaI detector using ANFIS. The summation of volume fractions is equal to 100% and is also a constant, and this is enough for the ANFIS just to forecast two volume fractions. In the paper, three ANFIS models are employed. The first network is applied to forecast the oil and water volume fractions. The next to forecast the water and gas volume fractions, and the last to forecast the gas and oil volume fractions. For the next step, ANFIS networks are trained based on numerical simulation data from MCNP-X code.
The accuracy of the nets is evaluated through the calculation of average testing error. The average errors are then compared. The model in which predictions has the most consistency with the numerical simulation results is selected as the most accurate predictor model. Based on the results, the best ANFIS net forecasts the water and gas volume fractions with the mean error of less than 0.8%.
The proposed methodology indicates that ANFIS can precisely forecast the volume fractions in a water-oil-gas three-phase flow system.
KeywordsStratified regime Three-phase flow Volume fraction Accuracy Fuzzy-based inference system Forecast
Compliance with ethical standards
Conflict of interest
The authors have no conflict of interest.
- 1.I.M.M. Babelli, Development of the multiphase meter using gamma densitometer concept, in Proceedings of the International Nuclear Conference (1997), pp. 371–389Google Scholar
- 23.G.H. Roshani, S.A.H. Feghhi, F. Shama, A. Salehizadeh, E. Nazemi, Prediction of materials density according to number of scattered gamma photons using optimum artificial neural network. Comput. Methods Phys. 2014, 305345 (2014)Google Scholar
- 32.J.F. Briesmeister, MCNP—a General Monte Carlo N-particle Transport Code, Version 4C. Report LA-13709-M (Los Alamos National Laboratory, April 2000)Google Scholar
- 34.J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, vol. 19 (Prentice Hall, Upper Saddle River, 1997), pp. 510–514Google Scholar
- 36.The MathWorks, Fuzzy Logic Toolbox User’s Guide, Inc., vol. 3 (Apple Hill Drive, Natick, 1995–2007), pp. 01760–02098Google Scholar
- 39.T. Yousefi, A. Karami, E. Rezaei, S. Ebrahimi, Fuzzy modeling of the forced convection heat transfer from a V-shaped plate exposed to an air impingement slot jet. Heat Transf. Asian Res. 41, 5 (2012)Google Scholar
- 41.M. Aghakhani, M.M. Jalilian, A. Karami, Prediction of weld bead dilution in GMAW process using fuzzy logic. Appl. Mech. Mater. 110—-116, 3171–3175 (2012)Google Scholar