Evaluation of Reference Equations of State for Density Prediction in Regasified LNG Mixtures Using High-Precision Experimental Data

TL;DR

This study compares three reference equations of state for predicting the density of regasified LNG mixtures, using high-precision experimental data to evaluate their accuracy across various conditions.

Contribution

It provides a comprehensive experimental validation of EoS models for RLNG, highlighting their accuracy and limitations depending on mixture composition.

Findings

AGA8-DC92 and GERG-2008 closely match experimental data within uncertainties.

SGERG-88 shows larger deviations, especially at low temperatures and high pressures.

Performance of SGERG-88 varies with mixture composition, sensitive to minor differences.

Abstract

This study evaluates the performance of three reference equations of state (EoS), AGA8-DC92, GERG-2008, and SGERG-88, in predicting the density of regasified liquefied natural gas (RLNG) mixtures. A synthetic nine-component RLNG mixture was gravimetrically prepared. High-precision density measurements were obtained using a single-sinker magnetic suspension densimeter over a temperature range of (250 to 350) K and pressures up to 20 MPa. The experimental data were compared with EoS predictions to evaluate their accuracy. AGA8-DC92 and GERG-2008 showed excellent agreement with the experimental data, with deviations within their stated uncertainty. In contrast, SGERG-88 exhibited significantly larger deviations for this RLNG mixture, particularly at low temperatures of (250 to 260) K, where discrepancies reached up to 3 %. Even at 300 K, deviations larger than 0.4 % were observed at high pressures, within the model's uncertainty, but notably higher than those of the other two EoSs. The analysis was extended to three conventional 11-component natural gas mixtures (labeled G420 NG, G431 NG, and G432 NG), previously studied by our group using the same methodology. While SGERG-88 showed reduced accuracy for the RLNG mixture, it performed reasonably well for these three mixtures, despite two of them have a very similar composition to the RLNG. This discrepancy is attributed to the lower CO2 and N2 content typical in RLNG mixtures, demonstrating the sensitivity of EoS performance to minor differences in composition. These findings highlight the importance of selecting appropriate EoS models for accurate density prediction in RLNG applications.