Combining simulated annealing and RBF networks for accurate volumetric fraction determination of two-phase flows

Abstract

The importance of flow monitoring in the oil industry has expanded due to the global need for fossil fuels. This has led to the emergence of a new subset of the flowmeter market. The goal of this study is to use a Radial Basis Function (RBF) neural network developed through Simulated Annealing (SA) to pick features of the signals generated by gamma-based flowmeters in order to determined volumetric fractions. The volumetric detection system presented in this article consists of a137Cs isotope as gamma emitter, two NaI detectors for collecting the photons, and a glass pipe in between them. Monte Carlo N-Particle (MCNP) was used to model the above-mentioned geometry. Fifteen wavelet, frequency, and time characteristics were extracted from the raw data captured by both detectors. First, the SA optimization algorithm was used to identify the suitable attributes. Five useful features were presented as a consequence of this procedure, and they were fed into the RBF network in order to estimate volumetric percentages. This study is innovative in that it combines the RBF neural network with the SA algorithm to pick effective features. The outcome is as follows: (1) Outlining five appropriate characteristics for use in determining percentages of volume. (2) Predicting the volume fraction of materials in two-phase flow with a root mean square error of less than 0.22. (3) By recognizing suitable inputs using the method based on the SA algorithm, the artificial neural network can determine the target output with less computational load.