275114 Large-Scale Computational Screening of Adsorbent Materials for Carbon Capture

Tuesday, October 30, 2012: 9:24 AM
405 (Convention Center )
Li-Chiang Lin1, Adam H. Berger2, Richard L. Martin3, Jihan Kim4, Joseph A. Swisher5, Kuldeep Jariwala6, Chris H. Rycroft6,7, Abhoyjit S. Bhown8, Michael W. Deem9, Maciej Haranczyk6 and Berend Smit5, (1)Chemical and Biomolecular Engineering, University of California-Berkeley, Berkeley, CA, (2)Electric Power Research Institute, Palo Alto, CA, (3)Computational Research Division, Lawrence Berkeley National Laboratory, (4)Lawrence Berkeley National Laboratory, Berkeley, CA, (5)Chemical and biomolecular Engineering, University of California-Berkeley, Berkeley, CA, (6)Lawrence Berkeley National Laboratory, (7)University of California-Berkeley, (8)Electric Power Research Institute , CA, (9)Rice University, Houston, TX

Carbon capture and sequestration (CCS) is an important strategy for reducing the negative environmental impact of energy derived from fossil fuels. The current technology to capture CO2, amine scrubbing, is very energy intensive and can decrease the efficiency of a coal-fired power plant by as much as 30%. Recently, nano-porous adsorbent materials such as zeolites, ZIFs, and MOFs have been found to potentially provide a more energy-efficient way for CO2 separation. In principle, there are hundreds of thousands of possible candidates that might be used for carbon capture. Fully characterizing all those possible materials with traditional molecular simulation technique is computationally prohibitive. We have developed a new computational approach to enable us to evaluate such large number of materials, across different classes, for their performance in carbon capture. In this study, we don’t rank the materials just based on a single adsorption property, such as selectivity or breakthrough time. Our metric, called parasitic energy, constitutes the minimized electric load which is imposed upon a power plant by using the material in a temperature-pressure swing capture process at it’s own optimal operation condition, followed by compression. For the determination of the parasitic energy, our computational procedure allows us to rapidly calculate all the important thermodynamic and geometric properties of a material. This efficient approach exploits the parallel processing power of graphics processing units (GPUs) as well as material informatics methods. For instance, the time required to calculate a COadsorption isotherm of a material over a wide range of pressure can be decreased by as much as two orders of magnitude compared to the traditional grand canonical Monte Carlo (GCMC) simulation on CPUs. From the outcome of this large-scale screening, we have identified a number of materials with an energy penalty almost 35% lower than the near-term technology. More importantly, insights about the optimal structures for carbon capture were found that should be very helpful for the future design and synthesis.

Reference:

L.-C. Lin et al., “In Silico Screening of Carbon Capture Materials”, Nature Materials, in press. (2012)


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See more of this Session: CO2 Capture by Adsorption-Adsorbents
See more of this Group/Topical: Separations Division