The ability to precisely estimate the void fraction of multiphase flow in a pipe is very important in the petroleum industry. In this paper, an approach based on our previous works is proposed for predicting the void ...The ability to precisely estimate the void fraction of multiphase flow in a pipe is very important in the petroleum industry. In this paper, an approach based on our previous works is proposed for predicting the void fraction independent of flow regime and liquid phase density changes in gas–liquid two-phase flows. Implemented technique is a combination of dual modality densitometry and multi-beam gamma-ray attenuation techniques. The detection system is comprised of a single energy fan beam,two transmission detectors, and one scattering detector. In this work, artificial neural network(ANN) was also implemented to predict the void fraction percentage independent of the flow regime and liquid phase density changes. Registered counts in three detectors and void fraction percentage were utilized as the inputs and output of ANN, respectively. By applying the proposed methodology, the void fraction was estimated with a mean relative error of less than just 1.2480%.展开更多
基金the financial support of Kermanshah University of Technology for this research under grant number S/P/T/1102
文摘The ability to precisely estimate the void fraction of multiphase flow in a pipe is very important in the petroleum industry. In this paper, an approach based on our previous works is proposed for predicting the void fraction independent of flow regime and liquid phase density changes in gas–liquid two-phase flows. Implemented technique is a combination of dual modality densitometry and multi-beam gamma-ray attenuation techniques. The detection system is comprised of a single energy fan beam,two transmission detectors, and one scattering detector. In this work, artificial neural network(ANN) was also implemented to predict the void fraction percentage independent of the flow regime and liquid phase density changes. Registered counts in three detectors and void fraction percentage were utilized as the inputs and output of ANN, respectively. By applying the proposed methodology, the void fraction was estimated with a mean relative error of less than just 1.2480%.
