Nuclear Technology and Energy

Prediction of Air and Water Flow-Rates Independent of Flow Regimes Using Gamma-Ray Attenuation Technique and Artificial Neural Network

https://doi.org/10.48308/nucte.2022.99022
Volume 1, Issue 1 - Serial Number 1
Spring 2022
Pages 49-54
Nuclear Technology and Energy

Authors

peyman Aarabi Jeshvaghani
Majid Khorsandi
Seyed Amir Hossein Feghhi

Shahid Beheshti University

Abstract
Gas-liquid two-phase flow is probably the most important form of multiphase flows and is found widely in the oil industry. The accurate prediction of the air and water flow-rates are important in two-phase flow. Nowadays, multiphase flow-rates measurement by gamma-ray attenuation technique is known as one of the most common precise methods. In this work, the air and water flow-rates independent of flow regime changes were accurately predicted within a two-phase flow loop in the laboratory. For this purpose, a combination of single beam gamma-ray, single detector and artificial neural network (ANN) were used in order to predict the flow-rates in the bubble, plug, slug, annular and dispersed regimes of gas-liquid two-phase flows. Two different types of neural networks (GMDH) were developed. The networks were developed based on four features extracted from recorded pulse height distribution in a dynamic condition. The result shows, air, and water flow-rates were measured with an average of Mean Relative Error (MRE) less than 4.5%. Overall results revealed that using the proposed method, gamma-ray attenuation technique combined with an ANN model can be efficiently used to predict the flow-rates. Furthermore, in this study, a new method based on a single beam, single energy, and the single detector was proposed in order to solve this problem, without any recalibration

Keywords

Two
phase flow
Gamma
ray attenuation
flow rate
Artificial Neural Networks
NaI(Tl) Detector
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