Publication
Title
Volume fraction determination of the annular three-phase flow of gas-oil-water using adaptive neuro-fuzzy inference system
Author
Abstract
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.
Language
English
Source (journal)
Computational and applied mathematics
Publication
2018
DOI
10.1007/S40314-018-0578-6
Volume/pages
37 :4 (2018) , p. 4321-4341
ISI
000443034900017
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 31.10.2020
Last edited 24.08.2024
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