278953 Integrating the Carbon Capture Materials Database with the Process Simulation Tools of the Carbon Capture Simulation Initiative

Wednesday, October 31, 2012: 3:35 PM
327 (Convention Center )
Hosoo Kim, Computational Science Division, National Energy Technology Laboratory, Morgantown, WV, Maciej Haranczyk, Computational Research Division, Lawrence Berkeley National Laboratory, Tom Epperly, Lawrence Livermore National Lab, Livermore, CA, Mahmoud Abouelnasr, Chemical and Biomolecular Engineering, University of California - Berkeley, Berkeley, CA, Joseph A. Swisher, Chemical and biomolecular Engineering, University of California-Berkeley, Berkeley, CA, Kuldeep Jariwala, Lawrence Berkeley National Laboratory, David Mebane, National Energy Technology Lab, Morgantown, WV, Berend Smit, Chemical and Biomolecular Engineering, University of California-Berkeley, Berkeley, CA, Joel Kress, Los Alamos National Lab, Los Alamos, NM and David C. Miller, U.S. Department Of Energy, National Energy Technology Laboratory, Morgantown, WV

Significant work is underway to identify new materials that could be effective for capturing CO2 from anthropogenic sources, such as coal-fired power plants, because the current technologies are too expensive. Among the potential materials being studied are various classes solid sorbents, such supported amines, zeolites and metal organic frameworks (MOFs). Since millions of potential materials could be developed, it can be useful to use computational techniques to identify the most promising. One of the primary goals of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI) is the development of state-of-the-art computational toolset to enable promising concepts to be more quickly identified through rapid computational screening of devices and processes. The components of the CCSI Toolset, which enable initial screening, consist of a set of detailed process models coupled with a process synthesis and design framework to facilitate development of complete, integrated carbon capture processes using advanced optimization techniques including deterministic superstructure-based methods and derivative-free optimization (DFO). Recently these models and optimization framework have been linked with Carbon Capture Materials Database (CCMDB, www.carboncapturematerials.org),  a comprehensive data collection on materials for carbon dioxide capture. The CCMDB has been developed by the Energy Frontier Research Center for Gas Separations Relevant to Clean Air Technologies, which is using computational chemistry methods to estimate thermophysical properties for crystalline porous materials such as zeolites and metal organic frameworks. The database currently contains data on approximately 200,000 materials. This presentation will describe how the properties from the CCMDB can be used within the CCSI models and optimization framework to screen the potential of new materials within the context of an optimized carbon capture process integrated with the other components of the energy generation system (i.e., power plant and compression system). Results from initial demonstration cases will also be discussed.

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