Pipeline transport of CO2 and H2 feedstocks across carbon transformation pathways – A comparative assessment of equation-of-state closures

The large-scale deployment of carbon capture, utilization and storage (CCUS) technologies requires reliable and efficient transport infrastructure capable of connecting CO 2 sources with utilization and storage facilities. The rightsizing and efficient operation of this infrastructure rely on the accurate prediction of the transient flow behavior in CO 2 transport networks. This study investigates the impact of equation-of-state (EOS) selection on the accuracy and computational performance of a nonisothermal transient pipeline flow model applied to CO 2 -rich streams and hydrogen-containing energy carriers relevant to CO 2 -to-X pathways. The analysis covers the transport of CO 2 -rich mixtures under gaseous and supercritical conditions, as well as natural gas–hydrogen mixtures, across pressure ranges representative of onshore and offshore pipeline systems. Six EOS formulations, including Helmholtz-energy-based, virial-expansion, corresponding-state, and cubic models, are evaluated. The transient flow model and the selected EOS closures are validated using field measurements from an operational CO 2 transport network. Predictions of pressure, temperature, chemical energy flow rate, and pipeline linepack are further benchmarked against reference calculations based on GERG-2008 and EOS-CG formulations. The results demonstrate that appropriate selection of EOS can significantly improve computational efficiency while maintaining the accuracy required for engineering applications. Simplified formulations achieve permissible prediction errors while reducing computational time by approximately one order of magnitude. The proposed modeling framework enables an efficient assessment of transport capacity, operational flexibility, and linepack management in CO 2 and hydrogen transport systems, supporting the development of integrated CCUS infrastructure and large-scale carbon utilization pathways.