Modeling of CO2 Flow with Impurities

Abstract

Carbon Capture, Utilization, and Storage (CCUS) plays a pivotal role in reducing greenhouse gas emissions, essential for limiting global warming to below 1.5 °C by 2100 and achieving carbon neutrality by 2050. Modeling carbon dioxide (CO₂) density is crucial for optimizing CO₂ transportation and storage systems. However, captured CO₂ streams, often originating from power sources, contain impurities such as Oxygen (O₂), Nitrogen (N₂), Carbon Monoxide (CO), Argon (Ar), Sulfur Dioxide (SO₂), Hydrogen (H₂), Methane (CH₄), Water (H₂O), and Hydrogen Sulfide (H₂S). These impurities significantly impact transmission properties and challenge the predictive capabilities of current equations of state (EoS). To address these challenges, this study utilized a comprehensive dataset comprising 134,204 density data points to evaluate the performance of 14 EoSs, including cubic, virial, physical, and multi-parameter equations. A versatile computational framework was developed, capable of calculating a range of thermodynamic properties beyond density, such as fugacity and its derivatives, enthalpies, and pressure derivatives. Within the development process, various numerical solvers were implemented to enhance computational efficiency, and genetic algorithms were adopted for better predictive performance. This robust framework provides researchers with a powerful tool for advancing CCUS technology, laying the foundation for future studies in thermodynamic modeling and system optimization. For density predictions, the research adopted a dual approach. Machine learning (ML) techniques, including Random Forest, Gradient Boosting, and Neural Networks, were employed to enhance predictive accuracy. These models demonstrated robust performance across complex thermodynamic regions by leveraging a combination of experimental and synthetic data (R² > 0.96). Synthetic data, generated within CCUS pipeline operating conditions using the best-performing EoSs, primarily multi-parameter equations, exhibited an Absolute Average Relative Deviation below 3%. On the other hand, the second approach utilized ranking EoS performance across temperature, pressure, and composition intervals, analyzed using Pareto plots and decision trees with Entropy and Gini indices. This systematic framework identified the optimal applicability ranges for each EoS, shedding light on their strengths and limitations. The integration of ML with interval-based sorting allowed for precise density predictions and reliable assessments of model stability under diverse conditions. The scope of the study extended to stability analysis, evaluating thermodynamic behavior and ensuring reliable predictions critical for the safe and efficient operation of CCUS systems. By assessing phase behavior, stability limits, and operational conditions, the study addressed key challenges in CO₂ pipeline systems and laid the groundwork for their effective design and management. Building on these insights, the research applied the findings to optimize CO₂ transmission networks. A case study was conducted, exploring mass and molar balances, energy balances, system stability, pressure and temperature drops, and viscosity modeling. This comprehensive analysis provided actionable insights into the operational dynamics of CO₂ pipelines, enabling precise predictions and optimizations for real-world applications. To the best of our knowledge, no previous study has utilized such an extensive dataset, evaluated such a diverse array of EoSs, or incorporated this innovative hybrid approach alongside practical case studies and a computational framework of this breadth. These findings provide a robust platform for improving the efficiency, reliability, and scalability of CO₂ transportation and storage systems, further supporting CCUS’s pivotal role in achieving global climate objectives