(34b) Elucidating Lignin Pyrolysis Using Kmc Modeling

Advanced understanding of lignin pyrolysis is limited by the complex nature of the substrate and the process, requiring elaborate characterization and quantification. Computational approaches can provide details not directly accessible experimentally. For detailed kinetic models to be able to simulate pyrolysis, they need to describe lignin structure and connect it to the product distribution by a serviceable reaction network.

In this work, we built on previous efforts in our group to describe the deconstruction of lignin through pyrolysis at the pathways level^1-3. First, model lignin structures are generated by stochastically growing polymer chains defined by monomer, bonding type, and bonding position. For example, previous work simulated three types of bonds, β-O-4, β-5, and 5-5, as they represent the highest population of bonds observed in lignin. Lignin structures were pooled into libraries that were optimized to fit experimentally determiner parameters – M_w, M_n, monomer percentage, bond type percentage, and branching coefficient. A kinetic model at the pathways level was formulated by assigning specified reaction families to lignin substructures and functional groups. The structural and kinetic models were coupled by a kinetic Monte Carlo framework to simulate lignin pyrolysis. Kinetic rate parameters for the reaction families were optimized by comparison to experimental data and product yields tracked.

In our most recent work, capabilities of the model were examined further and extended by simulating pyrolysis of a model lignin tetramer studied experimentally for elucidating primary products. The model could describe the product structures obtained experimentally and allowed for studying the temporal evolution of species. In addition, scrutiny of the relative contributions of different reaction families to pyrolysis under different operating conditions and feedstock sources was possible.

1. A. Yanez et al, Energy Fuels, 2016, 30, 5835-5845
2. L. Dellon et al, Energy Fuels, 2017, 31, 8263−8274
3. A. Yanez et al, Energy Fuels, 2018, 32, 1822-1830