Catalyst deactivation via coking is a major roadblock in the catalytic upgrading of pyrolysis bio-oil.1
Coking is a result of a number of interdependent factors ranging from catalyst properties (porosities, sizes, architectures) as well as process conditions (nature of feed, temperatures), which span very broad length and time scales.2,3
Meso-scale particle based multi-physics models have the ability to bridge across these scales by incorporating the chemical insight derived from ab-initio
atomistic modeling techniques in conjunction with requisite physics accounting for transport effects to effectively guide catalyst design. One of the major challenges to setting up meso-scale models is the non-availability of true and absolute kinetic parameters (i.e. devoid of physics) for lumped kinetic schemes to describe catalyst deactivation. Kinetic parameters available in literature and derived from experimental observations (in the case of bio-oil4,5
) do not decouple transport effects from intrinsic kinetic rates. This delineation between kinetics and transport resistance to a reaction are crucial to effectively guide catalyst synthesis as well as reactor operations. Towards this, we present a new lumped reaction mechanism, informed by experiment, for catalytic fast pyrolysis of bio-oil over H-ZSM5 that accounts for important product species (hydrocarbons, furans, phenols etc.) as well as deactivation by coking. The intrinsic kinetic rates of reaction for the mechanism are calculated by iteratively optimizing the rate parameters in the meso-scale multi-physics model, informed by experimentally characterized physical properties (porosities, bulk densities, active site concentrations etc.), to reproduce experimentally observed product evolution and coke formation.
1 Mukarakate, C. et al. Real-time monitoring of the deactivation of HZSM-5 during upgrading of pine pyrolysis vapors. Green Chemistry 16, 1444-1461 (2014).
2 Hansen, N. & Keil, F. J. Multiscale modeling of reaction and diffusion in zeolites: from the molecular level to the reactor. Soft Materials 10, 179-201 (2012).
3 Keil, F. J. in Multiscale Molecular Methods in Applied Chemistry (eds Barbara Kirchner & Jadran Vrabec) 69-107 (Springer Berlin Heidelberg, 2012).
4 Adjaye, J. D. & Bakhshi, N. N. Catalytic conversion of a biomass-derived oil to fuels and chemicals II: Chemical kinetics, parameter estimation and model predictions. Biomass and Bioenergy 8, 265-277, doi:https://doi.org/10.1016/0961-9534(95)00019-4 (1995).
5 Adjaye, J. D. & Bakhshi, N. N. Catalytic conversion of a biomass-derived oil to fuels and chemicals I: Model compound studies and reaction pathways. Biomass and Bioenergy 8, 131-149, doi:https://doi.org/10.1016/0961-9534(95)00018-3 (1995).
6 Zhang, H., Wang, Y., Shao, S. & Xiao, R. Catalytic conversion of lignin pyrolysis model compound-guaiacol and its kinetic model including coke formation. Scientific reports 6, 37513 (2016).