Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows

Feasibility Study of Using X-ray Tube and GMDH for Measuring Volume Fractions of Annular and Stratified Regimes in Three-Phase Flows

“…The relatively low errors of the proposed networks demonstrated the ability of an X-ray source and GMDH neural network as a promising metering system in three-phase flows. The obtained results indicate that the obtained measurement precision for the gas and water volume fractions in the present work were improved by more than two times compared to the previous study [17].…”

“…Hence, every attempt aimed at reducing the number of detectors in a photon attenuation-based system with the condition that the system's performance is not decreased is of great importance. Besides, in the present work, the gas and water volume fraction measurement precision has been improved by more than two times compared to the system in the previous study [17].…”

“…They indicated that the hybrid network that uses the oil and gas volume fractions as its outputs is the best estimator model for estimating volume fractions of gas, oil, and water in a stratified regime of a three-phase flow. Further research about multiphase flow meters and the application of artificial intelligence in different engineering fields can be found in references [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

“…In a recent study [17], an X-ray tube was used for simultaneously determining flow regime and volume fraction; two detectors were implemented in the system. In this paper, a simple system including just one detector and one X-ray tube is presented for measuring volume fractions of gas, oil, and water separately in two typical flow regimes of threephase flows (annular and stratified).…”

Evaluation of Organ Dose and Image Quality Metrics of Pediatric CT Chest-Abdomen-Pelvis (CAP) Examination: An Anthropomorphic Phantom Study

“…In addition, artificial intelligence has lately been commonly used to optimize radiationbased processes and has several benefits over conventional approaches. The usage of artificial intelligence contributes to refining photon radiation-based applications in both the medical and manufacturing industries [47][48][49]. Likewise, Machine Learning and Deep Learning a subset of artificial intelligence have been used in a number of applications to evaluate complicated data sets and to identify similarities and associations within those data without being directly configured [50,51].…”