Gas-Non-Newtonian Liquid Flow Through Horizontally Oriented Helical Coils-Prediction of Frictional Pressure Drop Using ANN

Helical coils are extensively used in different process industries. The two-phase flow through the coils is more complex than that of straight pipe due to presence of centrifugal forces, the flow through coils is always developing in nature. The knowledge of frictional pressure drop is an important hydrodynamic parameter used for the designing the coil. The applicability of the Artificial Neural Networks (ANN) methodology was investigated using experimental data obtained from our earlier studies on the frictional pressure drop for gas-non-Newtonian liquid flow through helical coils in horizontal orientation. Multilayer Perceptron (MLP) trained with back propagation algorithm using four different transfer functions in a hidden layer is used in ANN. Statistical analysis is used for the comparison to identify the best network. The ANN’s capability to predict the two-phase frictional pressure drop across the coils is one of the best estimation methods with high accuracy.