Solid–Liquid–Vapor Equilibrium Models for Cryogenic Biogas Upgrading

Abstract

International audienceDesign and optimization of cryogenic technologies for biogas upgrading require accurate determination of freeze-out boundaries. In cryogenic upgrading processes involving dry ice formation, accurate predictions of solid–liquid, solid–vapor, and solid–liquid–vapor equilibria are fundamental for a correct design of the heat exchanger surface in order to achieve the desired biomethane purity. Moreover, the liquefied biogas production process, particularly interesting for cryogenic upgrading processes due to the low temperature of the obtained biomethane, requires an accurate knowledge of carbon dioxide solubility in liquid methane to avoid solid deposition. The present work compares two different approaches for representing solid–liquid, solid–vapor, and solid–liquid–vapor equilibria for the CH4−CO2 mixture. Model parameters have been regressed in order to optimize the representation of phase equilibrium at low temperatures, with particular emphasis to the equilibria involving a solid phase. Furthermore, the extended bibliographic research allows determining the regions where more accurate data are needed

Energy and Economic Analysis of a New Low-Temperature Distillation Process for the Upgrading of High-CO2 Content Natural Gas Streams

“…For the PT diagram of the SLV locus for the binary CH 4 −CO 2 system (Figure 3a), all the methods have difficulties reproducing correctly the behavior in the proximity of the maximum. Classic approaches tend to overestimate the maximum pressure of the SLV locus, as observed also in other literature works 58 where these methods are used, while the process simulator utility tends to slightly underestimate this value.…”

Application of artificial neural networks (ANN) for vapor‐liquid‐solid equilibrium prediction for CH4‐CO2 binary mixture

“…The Agrawal and Laverman 11 data for 10.8% CO 2 are also fairly good with 1.98 AAD% and 0.9449 R 2 while for 3% CO 2 the deviation is higher for higher temperatures. Le and Trebble 12 data are not in good agreement with the model, which is due to the inconsistency of the data highlighted by Riva et al 15 The statistical analysis of the model and experimental data in vapor-solid equilibrium is presented in Table 5. Figure 8 presents the quantitative analysis of the vapor phase and solid formation of CO 2 in the V-S region along with the validation using Pikaar et al 8 Here, the developed ANN model utilized Pikaar isotherm data at −63, −70, −80, −90, −100, and −120°C, which consisted of a total of 60 data points for training, testing, and validation.…”

“…The Agrawal and Laverman data for 10.8% CO 2 are also fairly good with 1.98 AAD% and 0.9449 R 2 while for 3% CO 2 the deviation is higher for higher temperatures. Le and Trebble data are not in good agreement with the model, which is due to the inconsistency of the data highlighted by Riva et al . The statistical analysis of the model and experimental data in vapor‐solid equilibrium is presented in Table .…”

“…They concluded that N 2 and C 2 H 6 had little effect on the CO 2 frost point. Another notable work using PR EOS was reported by Riva et al . In this, two thermodynamic models were developed and different approaches were used to predict the solid‐liquid, solid‐vapor, and solid‐liquid‐vapor equilibria.…”

“…The mole fraction of CO 2 was higher than in previous experimental data. Moreover, a thermodynamic model using the Soave-Redlich-Kwong (SRK) equation of 15 In this, two thermodynamic models were developed and different approaches were used to predict the solid-liquid, solid-vapor, and solid-liquid-vapor equilibria. Hlavinka et al 16 used a Promax R process simulator to predict the CO 2 freezing temperature in a binary system of CH 4 -CO 2 .…”