EXPERIMENTAL AND MODELLING STUDY OF CO2 GASIFICATION OF CORN STOVER CHAR USING CATALYST

CO₂ concentration in the atmosphere poses a great threat to life on earth as we know it. The reduction of CO₂ concentration is key to avoid the critical turning point of 1.5^oC temperature increase highlighted by Intergovernmental Panel on Climate Change (IPCC). Gasification using CO₂ as reacting agent can potentially reduce the CO₂ concentration in the atmosphere. Naturally, biomass such as corn, uses great amounts of CO₂ for photosynthesis and produces O₂; hence, energy and fuel production using biomass can potentially be classified as carbon neutral. Moreover, if CO₂ is used as the gasifying agent, gasification can effectively be carbon-negative and collaborate to the reduction of CO2 in the atmosphere.

The major setback of using CO₂ biomass gasification is the energy-intensive reaction between C + CO₂ -> 2CO. This reaction at atmospheric pressure and room temperature is heavily tilted towards producing char and CO2. The current investigation describes efforts to favor the right hand side of the reaction by using simple impregnation techniques and cost-effective catalysts to reduce the energy requirements of the reaction. Also, parameters such as pressure are explored to tilt the balance towards the production of CO. Corn stover is selected as the biomass for the present research due to its wide use and availability in the US.

The results show that by using catalysts such as iron nitrate and sodium aluminate, the temperature required to achieve substantial char conversion is reduced. Also, increasing the pressure of the reactor, the temperature can be substantially decreased by 100 K and 150 K. The structure and chemical composition of the chars is studied to explain the differences in the reaction rate between chars. Further, chemical kinetics are calculated to compare the present work with previous work in the literature. Finally, data-driven analysis of the gasification data is explored. The appendices provide supplementary information on the application of deep learning to CO₂ recycling using turbulent flames and efforts to reduce the flame spread rate over a pool of Jet A by using Multi Walled Carbon Nanotubes (MWCNTS).