(519c) Coupled Near Infrared Spectroscopy and Air Classification of Corn Stover for Improved Feedstock Quality

A principal challenge of both lignocellulosic and petroleum-based fuel production is heterogeneity of feedstock materials. While both biomass and crude oil require separation to produce more homogeneous fractions, the latter has centuries of technical development. To be competitive, front-end biomass separation must be scalable and inexpensive: constraints met by pneumatic separation (air classification). Air classification leverages differences in density and aerodynamic profiles of biomass anatomical tissues to homogenize the feedstock. Near infrared spectroscopy (NIRS) enables high throughput composition analysis that can be correlated to anatomical tissue type. Here, we demonstrate that a physics-based pneumatic separation model enhanced with NIRS can inform front-end separation processes to improve feedstock quality. Our work shows that NIRS predicts anatomical tissue composition of corn stover while calculated partition velocities predict observed air classified corn stover fractions. Coupling these independently powerful tools advances separation technology to a state that can be leveraged by industry to enable cost-competitive, at-scale biofuels.