Holistic Kinetic Study of the Reduction of CH4 with NiO in Chemical-Looping Combustion

The objective of this work is to compare reaction schemes and kinetic mechanisms that accurately describe the dynamic behavior in chemical-looping reducers, operating with CH₄ and NiO/support (oxygen carrier).

The reaction scheme proposed is composed of catalytic gaseous reactions and heterogeneous reactions with the oxygen carrier include reactions of CH₄, CO and H_2. Ni-catalyzed gaseous reactions considered in this scheme include steam reforming, overall steam reforming, water gas shift, dry reforming, methane decomposition and carbon gasification by CO₂ and H₂O.

Kinetic expressions reported in the literature are compared and their parameters are estimated on the basis of experimental data of Ni-based oxygen carriers for chemical-looping combustion of CH₄. Rates of heterogeneous reactions based on shrinking core model and modified volumetric model with reported data from literature are compared.

Dynamic parameter estimation is performed to estimate kinetics of Ni-based oxygen carriers for chemical-looping reduction. Pre-exponential factors and activation energies for the various mechanisms reported reactions of the scheme proposed are fitted to dynamic data of chemical-looping reduction of NiO using CH₄. CLC data from the literature vary in terms of reactor volume, space velocity, and temperature, generating a sufficient span of conditions to explore the kinetic mechanisms most accurate for CLC. The consistency and discrepancy between the best fit kinetic parameters of different units will be presented.

The capability of the model to predict the gas composition at various operating conditions and reactor configurations using the same reaction mechanisms will be shown, allowing for confidence in the kinetics used in other types of reactor models (e.g., bubbling beds, risers, circulating fluidized beds) and a comprehensive analysis of the scale-up capabilities of CLC reducers.

Acknowledgement: This material is based upon work supported by the National Science Foundation under Grant No. 1054718.