A neural network model for prediction of liquid holdup in two-phase horizontal flow

Accurately predicting the liquid holdup associated with multiphase flow is a critical element in the design and operation of modern production systems. This prediction is made difficult by complex phase distributions and the wide range of fluid properties encountered in production operations. Consequently, the performance of existing correlations is often inadequate in terms of desired accuracy and application range. This investigation focuses on the development of a neural network model, a relatively new approach that has been applied successfully to a variety of complex engineering problems. Data from five independent studies were used to develop a neural network for predicting liquid holdup in two-phase horizontal flow. A detailed comparison with existing empirical correlations and mechanistic models reveals that for this data set, the neural network model shows an improvement in overall accuracy and performs more consistently across the range of liquid-holdup and flow patterns.