Gas-liquid multiphase flow measurement in venturi tube through data-driven modelling

Precise quantification of multiphase flow rates holds paramount significance in the context of process surveillance and enhancement in the energy sector. Conventional methodologies depend on the physical partitioning of phases prior to the use of single-phase meters, presenting a labor-intensive and economically demanding procedure. Recent developments in the field of machine learning present innovative data-driven methodologies for approximating multiphase flow rates by leveraging sensor-derived measurements. This research explores the examination of neural network architectures, specifically exploring deep neural networks (DNN) and convolutional neural networks (CNN), with the aim of predicting multiphase flow rates in Venturi tubes. Temporal data series and mean values pertaining to variables such as differential pressure, temperature, as well as throat and recovery differential pressure serve as inputs for the model. The primary objective of these data-centric methodologies is to ascertain gas and liquid flow rates directly, eliminating the need for the identification of flow patterns. Both instantaneous and time-averaged predictions are studied. Academic parlance entails subjecting models to training and testing processes using empirical datasets across diverse multiphase flow scenarios. The findings unequivocally establish the viability and efficacy of the suggested DNN and CNN architectures for addressing the complexities inherent in this demanding application. Accuracy is gauged using MSE, RMSE, MAE, and R-squared to assess the disparities between predictions and reference measurements. The enhancement of sensor inputs, customization of network architectures, and the implementation of field testing are integral aspects within the purview of Outlook. These measures are undertaken to bolster resilience across various facilities and operating conditions, thereby contributing to an augmented level of robustness.