| description abstract | Precisely forecasting the operational characteristics of oil pipelines is essential for developing rational design, production, and operation strategies, as well as reducing energy consumption and saving energy. Due to significant disparities in the computation outcomes of conventional mechanism models and the inadequate performance of machine learning models when handling limited sample data, their conclusions likewise lack tangible significance. In this study, a novel physics-guided neural network (PGNN) model, which integrates mechanisms with machine learning models, is introduced. The proposed model incorporates essential physical intermediate factors that impact the temperature and pressure of oil pipelines as artificial neurons within the loss function. Additionally, an adaptive moment estimate approach is employed to optimize the parameters of the model. Through a comparative analysis of various models' predictive capabilities on an oil pipeline, it was shown that PGNN has the highest level of accuracy in forecasting pipeline temperature and pressure. Furthermore, PGNN demonstrates the ability to generate satisfactory prediction outcomes even with a limited sample size. Simultaneously, the predictive outcomes of PGNN exhibit a stronger correlation with variables that have a direct impact on temperature and pressure. | |
