(400e) CO2 Removal From Natural Gas: Combined Materials Screening and Process Optimization
The removal of CO2 from natural gas is an important purification step to enhance energy content and meet pipeline specifications. There are vast natural gas reserves with high CO2 content that are uneconomical to develop with current technology. Additionally, CO2 removed at the wellhead is typically released into the atmosphere, making natural gas production the second-largest source of CO2 emissions in the U.S (after fossil fuel consumption) . Novel technology to separate and capture CO2 from natural gas has the potential to open new opportunities for domestic energy production while reducing cost and CO2 emissions.
Microporous adsorbents, such as zeolites and metal-organic frameworks, have been proposed for the selective separation of CO2 from natural gas, due to their abilitiy to act as molecular sieves. We have developed the first computational approach to combine materials screening with process optimization to identify the most cost-effective sorbents for this separation based on their performance in a realistic pressure-swing adsorption (PSA) process.
Our hierarchical method [2-3] consists of material characterization through ZEOMICS  and MOFomics , filtering based on novel selectivity metrics [6-9], and PSA process optimization via a rigorous nonlinear algebraic and partial differential equation (NAPDE) model . We have discovered novel materials for cost-effective processing of natural gas with various CO2 contents up to 50-60%. We also consider the purification of landfill gas, created by the decomposition of waste, which is primarily a mixture of methane and carbon dioxide after drying. Natural gas is obtained with at most 2-3% CO2, suitable for pipeline transport, while the captured CO2 is compressed to 150 bar for transportation or storage.
1. U.S. Energy Information Administration, Report Number DOE/EIA-0573(2009), 2011.
2. Hasan MMF, First EL, and Floudas CA. Cost-effective CO2 capture based on in silico screening of zeolites and process optimization. Submitted for publication.
3. U.S. Provisional Patent Application #61/761,436 and #61/765,284.
4. First EL, Gounaris CE, Wei J, and Floudas CA. Computational characterization of zeolite porous networks: an automated approach. Phys. Chem. Chem. Phys. 13:17339-17358, 2011.
5. First EL and Floudas CA. MOFomics: Computational pore characterization of metal-organic frameworks. Micropor. Mesopor. Mater. 165:32-39, 2013.
6. Gounaris CE, Floudas CA, and Wei J. Rational design of shape selective separation and catalysis-I: Concepts and analysis. Chem. Eng. Sci. 61:7933-7948, 2006.
7. Gounaris CE, Wei, J, and Floudas CA. Rational design of shape selective separation and catalysis-II: Mathematical model and computational studies. Chem. Eng. Sci. 61:7949-7962, 2006.
8. Gounaris CE, Wei J, Floudas CA, Ranjan R, and Tsapatsis M. Rational design of shape selective separations and catalysis: Lattice relaxation and effective aperture size. AIChE J. 56:611-632, 2009.
9. First EL, Gounaris CE, and Floudas CA. Predictive framework for shape-selective separations in three-dimensional zeolites and metal-organic frameworks. Langmuir 29:5599-5608, 2013.
10. Hasan MMF, Baliban RC, Elia JA, and Floudas CA. Modeling, simulation, and optimization of postcombustion CO2 capture for variable feed concentration and flow rate. 2. Pressure swing adsorption and vacuum swing adsorption processes. Ind. Eng. Chem. Res. 51:15665-15682, 2012.