Post Combustion CO2 Capture Through Adsorption Process In Fixed And Fluidized Beds

No Thumbnail Available
Journal Title
Journal ISSN
Volume Title
Increasing CO2 emissions in the environment leads to global warming which is an issue of great concern today. This awareness of increase in greenhouse gas emissions has resulted in the development of new technologies which would lower emissions of CO2 from flue gas. At present, there are three major approaches for CO2 capture: pre-combustion capture, oxy-fuel process and post-combustion capture. Post combustion capture offer some advantages as existing combustion technologies can still be used without radical changes on them. This makes post combustion capture easier to implement as a retrofit option compared to the other two approaches. Therefore, post combustion capture is the technology that will probably be deployed. In the present work, CO2 capture by post combustion technology using adsorption process in fixed as well as fluidized beds was studied. A comprehensive set of data and analysis for CO2 adsorption equilibrium and kinetics is presented for zeolite 13X, zeolite 5A, zeolite 4A and activated carbon (coconut fibre based). Adsorption test had been carried out at a temperature ranging from 298 K to 333 K and CO2 partial pressure of up to 0.4 MPa. The experimental data were correlated as a function of temperature and pressure to fit with different model equations such as (Langmuir, Freunlich, Toth and Sips). The thermodynamics of CO2 adsorption was also estimated for individual adsorbents using Van’t Hoff’s equation. The fixed bed adsorption process was simulated by considering a linear driving force (LDF) for the overall mass transfer and diffusivity factor. The LDF model acceptably reproduced all of the breakthrough curves and can be considered as adequate for designing an adsorption process to separate CO2 from flue gas. The model sensitivity was analyzed by varying parameters such as bed height, flow rate, bed void fraction, and adsorbent pellet diameter to better understand the model.
Supervisor: Pinakeswar Mahanta