Simulation-Based Engineering of Biomass Fast Pyrolysis Reactors | AIChE

Simulation-Based Engineering of Biomass Fast Pyrolysis Reactors


Conference Presentation

Conference Type

AIChE Annual Meeting

Presentation Date

November 19, 2020


17 minutes

Skill Level




Stuart Daw initiated the Computational Pyrolysis Consortium (CPC) over seven years ago (1) to help address the challenges posed by fast pyrolysis of biomass feedstocks and the catalytic upgrading of the bio-oil product. The multi-national laboratory consortium, with support from EERE’s Biomass Energy Technologies office, combined experimental and computational techniques to improve the fast pyrolysis process, improve bio-oil properties and component utilization, and develop tools and understanding of the scale-up and integration of component reactors supporting commercialization of catalytic fast pyrolysis systems. Since its inception, the CPC has broadened its membership and scope to address a range of biomass energy challenges as represented by its new name: Consortium for Computational Physics and Chemistry (CCPC) (2). At the invitation of Dr. Daw, NETL joined the CCPC in 2017 to assist in the application of multiphase computational fluid dynamics (CFD) tools to study pyrolysis and catalytic upgrading reactors. In this work, in collaboration with the CCPC, NETL uses its MFiX Suite of multiphase flow CFD tools to study two experimental pyrolysis systems being operated at DOE’s National Renewable Energy Laboratory. One reactor, a laboratory-scale bubbling fluid bed of biomass and sand, provides a platform to study fundamental pyrolysis over a range of biomass feedstocks and operating conditions. The second reactor is an entrained flow device for supplying pyrolysis gases for catalytic processing at the near-pilot scale. Particle drag models applicable to biomass, sand, and char were developed to accurately model solids hydrodynamics. A detailed pyrolysis chemistry mechanism (3) has been incorporated in the simulations to predict a broad range of product species that comprise the bio-oil, bio-gas, and bio-char products. Simulation results and comparison to experimental data are presented. Excellent agreement for both reactor systems was obtained. These tools are now being used to support development of pyrolysis reactor systems at larger scales using a broad range of forest product feedstocks.

  3. Debiagi, P., et al. (2018). A predictive model of biochar formation and characterization. Journal of Analytical and Applied Pyrolysis, 134, 326-335. doi: 10.1016/j.jaap.2018.06.022


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