(215h) A Multi-Scale Framework for Simulation of the Impact of Feedstock Variability on Fast Pyrolysis Products | AIChE

(215h) A Multi-Scale Framework for Simulation of the Impact of Feedstock Variability on Fast Pyrolysis Products


Parks, J. E. - Presenter, Fuels, Engines and Emissions Research Center, Oak Ridge National Laboratory
Rogers, W., NETL
Pecha, M. B., National Renewable Energy Lab
Wiggins, G., Oak Ridge National Laboratory
Ciesielski, P. N., National Renewable Energy Laboratory
Iisa, K., National Renewable Energy Laboratory
Wiatrowski, M., National Renewable Energy Laboratory
Shahnam, M., National Energy Technology Laboratory
Klinger, J., Idaho National Laboratory
Hartley, D., Idaho National Laboratory
An experimentally validated simulation framework has been constructed that can accurately predict product yield and chemistry for variable biomass feedstocks. The simulation framework contains models with a range of fidelity from computational fluid dynamics (CFD) to reduced-order and techno-economic analysis module model components. A complex chemical kinetics set developed by Debiagi et al. was utilized to capture chemistry differences in feedstocks. The kinetics set utilizes cellulose, hemicellulose, lignin, and ash composition of the biomass to predict pyrolysis yields for hardwood, softwood, and grass feedstocks. The model is able to predict the yield of individual chemical species instead of the typical lumped products of gas, tar, and char. Model validation occurred with an experimental data set focused on chemical variation of feedstocks resulting from anatomical fractions of loblolly trees. Validation of the models demonstrated that variations in biomass chemistry can be accounted for by the model with predictive capabilities generally in the +/5% accuracy range. However, the accuracy is somewhat subject to the method of accounting for condensables and water vapor. A key factor in the model’s ability to predict yields based on feedstock chemistry is the characterization of the C-, H-, and O-rich lignin fractions. Overall, the simulation framework has demonstrated robustness and utility for a diverse set of woody biomass samples.