Organisers

• Berend Smit (UC at Berkeley, USA)
• Sofia Calero (University Pablo de Olavide, Seville, Spain)
• Thijs J.H. Vlugt (Delft University of Technology, , The Netherlands)

Supports

   CECAM

   ESF-SimBioMa

Description

Thermodynamic arguments tells us that the most efficient separation of flue gasses requires a minimum of ca. 3.5% of the produced energy to capture the CO2 at a 15% concentration in flue gasses to obtain a 90% concentration of CO2 (1). In practice the energy costs are significantly higher than this thermodynamic limit. In fact, for the current technologies these costs are approximately 35% of the energy produced in a typical power plant. Similar analyses for typical chemical engineering processes shows that an energy costs of 3-5 times the thermodynamic minimum is achievable. 

The main capture technologies can be divided into three groups:

• Absorbent based technologies: in which a solvent captures the CO2. The energy costs are associated with the regeneration of the solvent. 

• Adsorbed based technologies: in which the solid adsorbent is used in, say, a pressure swing process. The energy costs for this process is the regeneration of the adsorbed by, for example, increasing the temperature or decreasing the pressure

• Membrane based technologies: in which a membrane is used to separate the gas molecules. The energy costs here are associated with pressurizing the gas or creating a vacuum. 

At present only the absorbent-based technologies are applied at a commercial scale. However, over the last few years there have been increasing research efforts in developing new materials for CO2 capture and it is expected that these research efforts will further expand in the coming years. This workshop aims to bring together computational scientists and some experts on CO2 capture technology, to discuss how computational techniques can contribute to the development of novel materials for these technologies.

At present several groups are working on computational techniques to study the properties of different materials in the context of CO2 capture. Examples, include 

CO2 absorption in ionic liquids (2) and adsorption of CO2 in nanoporous materials such as zeolites, zeolites (3-5) metal organic frameworks (MOF) (5-7) and zeolitic imidazolate frameworks (ZIFS) (8) A key aspect for the success of these materials is to design these materials with the optimal chemical composition together with the pore topology at the conditions one would like to separate CO2 from other gasses. The number of possible pore topologies is so large that it is difficult, if not impossible, to experimentally synthesize all possible structures. Computational methods can help in these screening efforts. One of the aims of the workshop is to explore whether new computational techniques need to be developed to make such a screening possible. 

Scientific Objectives

The objectives of the workshop include:

(1) Bringing together the different computational groups working on CO2 capture with the aim to arrive at a comparison of computational methods used in the various subfields. Are they sufficiently advanced that reliable predictions can be made? Where are improvements required? 

(2) What are the targets these materials should? How to defined an optimal material? What are the most promising strategies for finding the optimal adsorbents?

(3) Is it useful, adequate, and possible to extend this approach to include reactive solvents (i.e. amines) and reactive groups in porous media?

(4) How to deal with the very large number of materials that can potentially be synthesized? State of the art simulation techniques can only be used for a very limited set of materials, whereas as there are many millions of potentially interesting structures? Can prescreening methods be developed?

References

(1) Bhown, A. S.; Freeman, B Program on Technology Innovations: Post-combustion CO2 Capture Technology Development. EPRI, Palo Alto, CA: 2008. 1016995
(2) Cadena, C.; Anthony, J. L.; Shah, J. K.; Morrow, T. I.; Brennecke, J. F.; Maginn, E. J. J. Am. Chem. Soc. 2004, 126, 5300.
(3) Krishna, R.; van Baten, J. M. Chem. Eng. J. 2007, 133, 121.
(4) Garcia-Sanchez, A.; Ania, C. O.; Parra, J. B.; Dubbeldam, D.; Vlugt, T. J. H.; Krishna, R.; Calero, S. J. Phys. Chem. C 2009, 113, 8814.
(5) Liu, B.; Smit, B. Langmuir 2009, 25, 5918.
(6) Millward, A. R.; Yaghi, O. M. J. Am. Chem. Soc. 2005, 127, 17998.
(7) Walton, K. S.; Millward, A. R.; Dubbeldam, D.; Frost, H.; Low, J. J.; Yaghi, O. M.; Snurr, R. Q. J. Am. Chem. Soc. 2008, 130, 406.
(8) Banerjee, R.; Phan, A.; Wang, B.; Knobler, C.; Furukawa, H.; O'Keeffe, M.; Yaghi, O. M. Science 2008, 319, 939.