Study on prediction of vapor-liquid phase equilibrium of CO2, C1~C4 alkanols and their mixtures based on SAFT-type equations of state

WU Shihao; YANG Zhi; XIE Hanlin; HE Song; ZHENG Yawen; WANG Junyao; MO Songping

doi:10.12434/j.issn.2097-2547.20240220

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Study on prediction of vapor-liquid phase equilibrium of CO₂, C₁~C₄ alkanols and their mixtures based on SAFT-type equations of state

更新时间：2025-03-27

- Study on prediction of vapor-liquid phase equilibrium of CO₂, C₁~C₄ alkanols and their mixtures based on SAFT-type equations of state
- Vol. 50, Issue 3, Pages: 123-134(2025)
- 作者机构：
  
  1.广东工业大学材料与能源学院，广东广州 510006
  2.广东工业大学广东省生态安全与绿色发展卓越基础研究中心，广东广州 510006
  3.广东工业大学碳中和与绿色发展协同创新研究院，广东广州 510006
  4.深圳市燃气集团股份有限公司，广东深圳 518040
- 作者简介：
- 基金信息：
- DOI：10.12434/j.issn.2097-2547.20240220
  CLC： TQ013.1
- Received：20 May 2024，
  
  Revised：17 June 2024，
  
  Published：25 March 2025
- 稿件说明：
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吴世浩,杨智,谢汉林等.基于SAFT类状态方程的CO₂、C₁~C₄烷醇及其混合物气液相平衡特性的预测研究[J].低碳化学与化工,2025,50(03):123-134.

WU Shihao,YANG Zhi,XIE Hanlin,et al.Study on prediction of vapor-liquid phase equilibrium of CO₂, C₁~C₄ alkanols and their mixtures based on SAFT-type equations of state[J].Low-carbon Chemistry and Chemical Engineering,2025,50(03):123-134.
吴世浩,杨智,谢汉林等.基于SAFT类状态方程的CO₂、C₁~C₄烷醇及其混合物气液相平衡特性的预测研究[J].低碳化学与化工,2025,50(03):123-134. DOI： 10.12434/j.issn.2097-2547.20240220.

WU Shihao,YANG Zhi,XIE Hanlin,et al.Study on prediction of vapor-liquid phase equilibrium of CO₂, C₁~C₄ alkanols and their mixtures based on SAFT-type equations of state[J].Low-carbon Chemistry and Chemical Engineering,2025,50(03):123-134. DOI： 10.12434/j.issn.2097-2547.20240220.

摘要

相变吸收剂捕集CO

作为一种新型低能耗碳捕集技术已受到广泛关注。物理溶剂型相变吸收剂具有分相后富相溶液CO

负载更低的优点，具有较好的应用潜力。精确预测溶剂组分的相平衡特性对于相变吸收法CO

捕集系统的设计与优化至关重要。针对纯质体系（CO

、C

或C

烷醇）和二元混合体系（CO

+ C

烷醇、CO

+ C

烷醇、CO

+ C

烷醇或CO

+ C

烷醇），采用3种典型的统计缔合流体理论（SAFT）类状态方程（分别为CPA、PC-SAFT和SAFT-VR-SW状态方程），系统评估了不同缔合方案对纯质体系气液相平衡特性的预测性能。通过引入二元交互作用系数（

），探究了不同状态方程对二元混合体系气液相平衡特性的预测性能。结果表明，对于CO

，非缔合方案显示出更高的预测精度，其中采用非缔合方案的PC-SAFT状态方程的预测性能最优；对于C

烷醇，SAFT-VR-SW状态方程具有最大的预测偏差，而使用CPA或PC-SAFT状态方程时，不论是采用2B方案还是3B方案，整体无显著差异。当

为0时，3种状态方程对不同二元混合体系相平衡特性的预测存在显著差异，其中PC-SAFT状态方程对二元混合体系具有较好的预测性能。通过引入最优

，3种状态方程对二元混合体系相平衡特性的预测精度均显著提升，其中SAFT-VR-SW状态方程的提升最为显著，预测偏差从35.37%降至5.42%。

Abstract

Phase change absorbents for CO

capture have garnered significant attention as a novel low energy carbon capture technology. Physical solvent-based phase change absorbents exhibit the advantage of lower CO

loading of rich-phase solution after phase splitting

indicating strong application potential. Accurate prediction of the phase equilibrium properties of solvent components is crucial for the design and optimization of CO

capture systems by phase change absorption. Three typical statistical associating fluid theory (SAFT) equations of state (CPA

PC-SAFT and SAFT-VR-SW EoSs

respectively) were used to systematically evaluate the predictive performance of different association schemes on the vapor-liquid phase equilibrium properties for pure component (CO

or C

alkanol) and binary mixtures (CO

+ C

alkanol

+ C

alkanol

+ C

alkanol or CO

+ C

alkanol). By introducing binary interaction parameters (

)

the predictive performances of different EoSs for the vapor-liquid phase equilibrium properties of binary mixtures were investigated. The results indicate that for CO

the non-association scheme demonstrates higher predictive accuracy

with the PC-SAFT EoS providing the best performance. For C

alcohol to C

alcohol

the SAFT-VR-SW EoS shows the largest predictive deviation

while the CPA and PC-SAFT EoSs exhibit no significant differences

regardless of using the 2B or 3B association schemes. When

is 0

there is a significant difference in the phase equilibrium properties prediction of the three EoSs for different binary mixtures

with the PC-SAF

T EoS yields superior predictive capabilities. By introducing the optimal

the prediction accuracies of the phase equilibrium properties of the three EoSs for the binary mixtures are significantly improved

with the most notable improvement in the SAFT-VR-SW EoS

where the predicted deviation decreases from 35.37% to 5.42%.

关键词

Keywords

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