4 years ago

Magnetic Separation and Recycling of Ferrite Nanocatalysts for CO2 Decomposition with CH4 Recovery from Steel Industrial Flyash

Magnetic Separation and Recycling of Ferrite Nanocatalysts for CO2 Decomposition with CH4 Recovery from Steel Industrial Flyash
Over 0.2 million TPY (ton per year) of hazardous flyash is currently being produced by the steel industry in Taiwan, causing potential environment pollution issues. The decomposition and methanation reactions of CO2 with flowing H2 over oxygen-deficient recycled ferrites from steel industrial flyash (RFSIF) and nickel ferrite nanoparticles (NFNs) at 300–400°C and 1atm were studied and the fine structure of the Fe/Ni species in the RFSIF/NFN catalysts investigated. Experimentally, a magnetic separator was used to separate the magnetic ferrites from steel flyash with an average separation fraction of 74%. Oxygen deficiencies were created in RFSIF/NFNs by reduction with H2. The decomposition of CO2 into C and O2 was achieved within a few minutes upon contact with the oxygen-deficient RFSIF/NFNs by incorporation of O2 in the ferrites. Oxygen and carbon, rather than CO, were produced in the decomposition process. The complete decomposition of CO2 was possible due to the high number of oxygen deficiencies in the catalysts. The Fe pre-edge XANES spectra of RFSIF/NFNs exhibited an absorbance feature at 7115eV for the 1s-to-3d transition, forbidden by a selection rule in the case of perfect octahedral symmetry. The EXAFS data revealed that RFSIF/NFNs present two central Fe atoms coordinated primarily by oxygen with a bond distance of 1.93±0.02 and a coordination number of 4.03. CH4 was produced during the reactivation of RFSIF/NFNs with H2. Moreover, the decomposition of CO2 with the recovery of valuable CH4 using waste heat from the off-gas produced in the steel industry is an appealing alternative for energy recovery. Finally, the kinetic and thermodynamic parameters of the CO2 decomposition/methanation over RFSIF/NFNs at 300–400°C and 1atm were calculated using a pseudo first-order model and the Arrhenius equation, respectively.

Publisher URL: www.sciencedirect.com/science

DOI: S0920586117306314

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