Data-driven modeling of wax solubility in supercritical carbon dioxide and ethane systems

Abstract

Wax precipitation can present a persistent challenge in natural gas systems, leading to blockages, reduced flow efficiency, and costly interruptions across production, transportation, and processing units. Understanding and predicting the solubility of paraffin waxes in supercritical gases, such as CO₂ and ethane, is crucial to managing these flow assurance issues. However, conventional experimental techniques, while accurate, are resource-intensive and time-consuming. Similarly, traditional thermodynamic models often struggle with generalization when applied to complex multi-variable systems, particularly under varying operational conditions. In response to these limitations, this study explores the potential of advanced data-driven techniques to model wax solubility more efficiently and accurately. Three intelligent algorithms, including Extreme Trees (ET), Multi-Layer Perceptron optimized with Levenberg–Marquardt Algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), were trained and tested on a comprehensive experimental dataset that includes pressure, temperature, and critical temperatures of both the gas phase and solid wax compounds. Among these, the MLP-LMA model emerged as the top performer, achieving an outstanding prediction accuracy with an RMSE of 53.7729 and an R² of 0.9996. Further validation through trend analysis and XAI techniques (SHapley Additive exPlanations) confirmed the model’s ability to capture underlying physical patterns and variable importance. Leverage diagnostics also indicated strong statistical reliability, with only 1.98 % of observations classified as potential outliers. Beyond predictive accuracy, the model holds significant promise for real-world deployment. Its integration into industrial flow assurance systems could enable rapid solubility estimation, support operational decisions, reduce downtime, and optimize the design of wax management strategies in CO₂ and ethane-rich systems.