Liquid Transportation Distance Prediction Method for LNG Long-distance Pipeline Based on LSTM Algorithm

Abstract

Accurate prediction of liquid transportation distance in LNG long-distance pipelines is critical for optimizing energy transportation efficiency and ensuring operational safety. However, traditional methods often struggle to capture the nonlinear temporal dependencies and complex flow dynamics inherent in pipeline systems. This paper proposes a novel prediction method based on the Long Short-Term Memory (LSTM) algorithm to address these challenges. By leveraging historical operational data, including pressure, temperature, flow rate, and pipeline terrain characteristics, the LSTM model is trained to learn spatiotemporal patterns and predict transportation distances under varying conditions. The proposed framework incorporates data preprocessing, feature selection, and hyperparameter optimization to enhance model robustness. Experimental results demonstrate that the LSTM-based method outperforms conventional models in terms of prediction accuracy, achieving a mean absolute error of 0.82 km and a root mean square error of 1.15 km over a 200 km test pipeline segment. The model’s ability to handle long-term dependencies and noisy data highlights its practical applicability for real-time monitoring and decision-making in LNG pipeline management. This study contributes to advancing intelligent solutions for energy infrastructure optimization.