Lignocellulosic biomass is a promising feedstock for renewable fuels and chemical intermediates, potentially reducing greenhouse gas emissions as well as increasing the global energy supply and sustainability. Fast pyrolysis may offer technical and economic advantages over other biomass conversion technologies. Fundamental understanding of biomass composition, structure and reaction kinetics is crucial for improving the process design and control. Here, we investigated the structure and pyrolysis kinetics of hemicellulose and lignin, two out of three major components of biomass. First, we proposed a structure for hemicellulose extracted from cornstover based on the experimental characterization from the literature. Using a reaction family approach, continuous distribution kinetics framework, and previously obtained kinetic parameters for cellulose pyrolysis, a mechanistic model for fast pyrolysis of extracted hemicellulose was developed, which matched experiments closely and is extendable to native hemicellulose. Second, considering that lignin has a complex, amorphous structure with potentially hyper-branched topology, we developed a Monte Carlo algorithm to generate structural libraries of lignin molecules. Additionally, this approach allows for the investigation of feasible space of lignin structures, which satisfies the experimental characteristics simultaneously, including monomer, bond and molecular weight distributions as well as overall branching coefficient. Finally, we created a detailed mechanistic model for lignin pyrolysis via computational reaction network generation. The model is comprised of 4313 reactions and 1615 species based on lignin structures above and pyrolysis chemistry of model compounds. This mechanistic model was applied to simulate lignin pyrolysis via a Kinetic Monte Carlo approach, and it predicted dynamic product profiles and mechanistic reaction pathways, both reasonably cross-validated by the experiments.

Modeling structure and fast pyrolysis of biomass

256th National Meeting (2018)

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