Thermo-physical properties of CO2 mixtures and their impacts on CO2 capture, transport and storage: Progress since 2011. (1st December 2019)

• Record Type: Journal Article
• Authors: Li, Hailong; Dong, Beibei; Yu, Zhixin; Yan, Jinyue; Zhu, Kai
• Is Part Of: Applied energy. Volume 255(2019)
• Publication Date: 2019-12-01
• Subjects: CO2 capture transport and storage -- Thermo-physical properties -- Experimental data -- Modeling -- Property impacts; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113789
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The experimental data and theoretical models on thermo-physical properties of CO2 mixtures since 2009 are updated. The new knowledge gaps regarding measurements are identified. The performance of different property models are presented. The property impacts on carbon capture and storage are updated. Abstract: The knowledge of accurate thermo-physical properties is crucial for the development and deployment of CO2 capture, transport and storage (CCS). The progress on the experimental data and theoretical models regarding thermo-physical properties of CO2 mixtures as well as the property impact on the design and operation of different CCS processes has been updated. The newly published experimental data since 2011 have been collected and reviewed based on which the new knowledge gaps regarding measurements have been identified. There have also been some advanced models proposed recently, which have shown good performances. The collected model performances don't show there exist a model that is superior to others; but they still provide a good guideline regarding model selection. However, developing more-complex models as the complexity may not necessarily improve the accuracy when empirical parameters were included and well-tuned. By comparing the importance of the properties and the accuracy of existing models, suggestions were given regarding the development of property models that should be prioritized.