Chordal measurement of phase fraction distribution in a static gas-liquid system using collimated gamma-ray densitometer and artificial neural networks
Medición cordal de distribución de fracción de fase de un sistema estático gas-líquido usando densitómetro de rayos-gama colimado y redes neurales artificiales
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Two-phase flow occurs in various industries, as in the production of oil and gas. A collimated gamma-ray densitometer is applied for the study of a static gas-liquid system that simulates a stratified flow pattern. It stands out for its non-intrusive measurement capacity, its high sensitivity to density variations and its good spatial resolution. Chordal phase fraction distributions are obtained in a tube containing water and air at room conditions, with the water level varied between 25%, 50% and 75%. The results obtained highlight the usefulness of the collimated gamma-ray densitometer to determine phase fraction distributions along the pipe’s cross section. Furthermore, this study suggests the use of an artificial neural network (ANN) model for predicting holdup in pipeline systems using a dataset of 110 experimental data points. The ANN model considers factors such as absorbed intensity, water cut percentage, and dimensionless h/D ratio. The adopted configuration includes the use of the Adam solver, Rectified Linear Unit (ReLU) activation function, a batch size of 3, two hidden layers (60 neurons each), and a learning rate of 0.001. The model achieves good accuracy, with a minimum mean square error (MSE) of 0.3% and a low mean absolute error (MAE) of 0.028.
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