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Experimental Investigations on CO2 Recovery from Engine Exhaust Using Adsorption Technology
ISSN: 0148-7191, e-ISSN: 2688-3627
Published November 21, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: NuGen Summit
Energy policy reviews state that automobiles contribute 25% of the total Carbon dioxide (CO2) emission. The current trend in emission control techniques of automobile exhaust is to reduce CO2 emission. We know that CO2 is a greenhouse gas and it leads to global warming. Conversion of CO2 into carbon and oxygen is an energy-consuming process compared to the catalytic converters. The best way to reduce CO2 is to capture it from the source, store it and use it for industrial applications. To physically capture the CO2 from the engine exhaust, adsorbents like molecular sieves are utilized. In comparison to other CO2 separation methods, adsorption technique consumes less work and energy. Moreover, the sieves can be regenerated, reused and recycled once it is completely saturated. In this research work, zeolite X13 was chosen as a molecular sieve to adsorb CO2 from the exhaust. A chamber was designed to store the zeolite and it is attached to the exhaust manifold. The selected engine was a single-cylinder Briggs and Stratton petrol engine. The experiments were conducted in two phases, the first phase to adsorb and the second phase to regenerate. Temperature pressure swing adsorption was chosen as the preferred process for the regeneration. This study was conducted by varying chamber length in three measurements and sieve quantities. The gas separated from the sieves during regeneration is tested using AVL Ditest analyser to study the percentage of CO2 adsorbed from the engine exhaust. From the results, it was found that 70% of the CO2 emissions were absorbed using low cost zeolite sieves.
CitationS, S. and Ramesh Kumar, C., "Experimental Investigations on CO2 Recovery from Engine Exhaust Using Adsorption Technology," SAE Technical Paper 2019-28-2577, 2019, https://doi.org/10.4271/2019-28-2577.
Data Sets - Support Documents
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