An Optimized Artificial Neural Network Unifying Model for Steady-State Liquid Holdup Estimation in Two-Phase Gas–Liquid Flow

Simplifying assumptions and empirical closure relations are often required in existing two-phase flow modeling based on first-principle equations, hence limiting its prediction accuracy and in some instances compromising safety and productivity. State-of-the-art models used in the industry still include correlations that were developed in the sixties, whose prediction performances are at best acceptable. To better improve the prediction accuracy and encompass all pipe inclinations and flow patterns, we propose in this paper an artificial neural network (ANN)-based model for steady-state two-phase flow liquid holdup estimation in pipes. Deriving the best input combination among a large reservoir of dimensionless Π groups with various fluid properties, pipe characteristics, and operating conditions is a laborious trial-and-error procedure. Thus, a self-adaptive genetic algorithm (GA) is proposed in this work to both ease the computational complexity associated with finding the elite ANN model and lead to the best prediction accuracy of the liquid holdup. The proposed approach was implemented using the Stanford multiphase flow database (SMFD), chosen for being among the largest and most complete databases in the literature. The performance of the proposed approach was further compared to that of two prominent models, namely a standard empirical correlation-based model and a mechanistic model. The obtained results along with the comparison analysis confirmed the enhanced accuracy of the proposed approach in predicting liquid holdup for all pipe inclinations and fluid flow patterns.