A Comprehensive Neural Network Model for Predicting Two-Phase liquid Holdup under Various Angles of Pipe Inclinations

Accurate prediction of 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 the complexity of the distribution of the phases 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 range of application. This investigation focuses on the development of a neural network model, a relatively new approach that has been successfully applied to a variety of complex engineering problems. 2292 data sets from five independent sources were used to develop a neural network for predicting liquid holdup in two-phase flow at all inclinations from upward(+90 degrees) to downward(-90 degree) flow. A three-layer back propagation neural network has utilized. Seven parameters including inclination from horizontal, gas and liquid superficial velocity, diameter, liquid viscosity, density and liquid surface tension are used as inputs to the network. A detailed comparison with Mukherjee et al. and Beggs et al. correlations which are applicable for whole range of inclinations reveals that the developed model provides better accuracy and predicts liquid holdup in terms of the lowest absolute average percent error (9.407), the lowest standard deviation (8.544) and the highest correlation coefficient (0.9896).