Regime independent flow rate prediction in a gas-liquid two-phase facility based on gamma ray technique and one detector using multi-feature extraction

One of the key challenges in petroleum related industries is how to precisely measure the flow rates of individual phases in a pipeline. To address this challenge, in the present study, an automated two-phase test loop capable of generating different flow patterns in horizontal pathway is used to perform experiments on the flow rates. The measuring package set-up consists of a Cs-137 radiation source with photon energy of 662 keV and one NaI (Tl) scintillation detector to register transmission counts. Multi-layer perceptron (MLP) is the selected processing element. Distinguished property of this paper is considering a feature vector with diverse-nature elements as input and the flow rate values of water and air as the target elements. Several combinations of features were investigated to determine the feature vector which shows the best quality to predict the flow rates. Moreover, two structures of MLP with different scenarios of hidden layers were utilized for examining every feature vector set. Numerous experiments were performed to collect adequate data to train and test the ANN in a wide range of air and water flow rates. The results indicate that the proposed model achieved MAE of less than 1 and 5.9 L/min and MRE% of 1.09% and 1.45% to predict water and air flow rates, respectively. The results show that the presented ANN model outperforms existing methods on multiphase flow rates measurements. Therefore, the proposed feature extraction method is applicable to estimate phase flow rates in a two-phase flow for industrial goals.