4.5 Review

Towards first-principles based kinetic modeling of biomass fast pyrolysis

Journal

BIOMASS CONVERSION AND BIOREFINERY
Volume 7, Issue 3, Pages 305-317

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s13399-017-0251-0

Keywords

Reaction kinetics; Mechanistic model; Reaction mechanism; Kinetic parameters; Reaction network; Bio-oil

Funding

  1. European Research Council under the European Union's Seventh Framework Programme FP7/ERC [290793]
  2. Long Term Structural Methusalem Funding by the Flemish Government
  3. European Union's Horizon 2020 research and innovation programme [664876]
  4. Institute for Promotion of Innovation through Science and Technology in Flanders (IWT)
  5. European Research Council (ERC) [664876] Funding Source: European Research Council (ERC)

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Biomass conversion to chemicals and fuels through fast pyrolysis shows great potential but requires a more fundamental approach for its deployment. To this end, molecular-based kinetic modeling is starting to play a central role in the prediction of the molecular composition of bio-oil. A molecular-level representation of biomass provides the start point for the generation of detailed pyrolysis reaction networks for both the condensed and the gas phases. Significant progress has been made for cellulose, glucose-based carbohydrates, and lignin, together with the incorporation of the catalytic effects of minerals. Ab initio techniques are widely used to discriminate between reaction mechanisms and to calculate kinetic parameters. Automatic kinetic model generation is expected to play an even more important role in fast pyrolysis as it does already today. Experimental techniques enabled to obtain intrinsic kinetics and to decouple the timescales between reaction kinetics and analytic techniques. This greatly benefits the improvement of detailed kinetic models. The prospects for achieving a first-principles based kinetic model of biomass fast pyrolysis are promising. However, significant work is still needed to couple condensed- and gas-phase reaction networks.

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