(215b) Quantitative Study of Particle Attributes Effect on the Permeability and Reactor Scale-up in a Flow-through Packed Bed Processing of Lignocellulosic Biomass

Particle attributes have significant impact on the process reliability and capability of the conversion process of lignocellulosic biomass to biofuels. Due to the compressibility of the biomass material, the conventional permeability model cannot accurately describe the process behavior for lignocellulosic biomass in a flow-through process. We studied the difference in particle attributes for corn stover preprocessed at different moisture, different anatomy composition, and different particle sizes. We performed instrumented experiments to quantitatively characterize the material deformation effect in the flow-through process. A modified Kozeny Carman equation developed earlier was used to model the effect of different feedstocks on the permeability of the flow-through process. The process reliability at larger scale of using different feedstocks in a packed bed flow-through processing was studied using our compressible packed bed model. A preliminary neural network model was developed to predict key material parameters used in the compressible packed bed model from particle size information.