Liquid Holdup Prediction in Wet Natural Gas Pipelines Using an IASO‐Optimized BP Neural Network

ABSTRACT Liquid holdup is a key parameter for assessing liquid accumulation in undulating pipelines and is essential for ensuring their safe and reliable operation. To improve prediction accuracy, this study proposes an enhanced back propagation neural network (BPNN) optimized by an improved atomic search optimization (IASO) algorithm. The proposed IASO‐BP model was compared with several established approaches, including mechanistic models (MBII, BB, and BBE) and neural network models (BP, SSA‐BP, and ASO‐BP). Results show that the IASO‐BP model achieves superior performance, with a root mean squared error (RMSE) of 0.04478 and a mean absolute percentage error (MAPE) of 6.38%, outperforming both traditional mechanistic models and other neural network‐based benchmarks. Furthermore, a graphical user interface (GUI) was developed to integrate the model into user‐friendly prediction software, providing a practical tool for field applications.