Prediction of hydrate formation boundaries in pure water and salt/alcohol containing systems based on prior knowledge and artificial intelligence

Abstract

As an efficient and clean energy source, natural gas plays a crucial role in optimizing the global energy consumption structure. However, the formation and accumulation of hydrates in pipelines during the exploitation and transportation of natural gas greatly jeopardize the production safety. Accurately predicting hydrate formation boundaries remains challenging due to the complex interation among various influencing factors. Although mechanistic models provide insights, they often fall short in accuracy. Conversely, machine learning models, while promising, may face limitations related to small sample sizes and a lack of physical interpretability. To overcome these limitations, this study proposed a physically guided neural network (PGNN) based on the Chen-Guo model, which integrated thermodynamic mechanisms with a neural network framework. This proposed model utilized pressure calculated from the Chen-Guo model as an input variable and incorporates physical inconsistencies into its loss function. A comparative analysis using literature data that PGNN achieved superior prediction accuracy, with an R2 value of 0.9768 for gas hydrate formation pressure prediction. The integration of thermodynamic mechanisms enhanced the prediction accuracy, as evidenced by an increase in the R2 value by 0.036, and a reduction in the MSE value by 5.56. Furthermore, the PGNN maintained a high level of prediction accuracy, ranging from 0.96 to 0.98, even with limited sample sizes, thus confirming its applicability and stability. This study validated the feasibility of PGNN for predicting gas hydrate formation conditions and offered insights into hydrate-based applications and hydrate management strategies.