Predictive models enhance feedstock quality of corn stover via air classification
Feedstock heterogeneity is a fundamental obstacle to cost-competitive biobased products. Agricultural products like corn stover have anatomical components that vary in their chemical composition, mechanical properties, structure, and response to chemical and biological treatments. A technique that can enrich streams in select anatomical fractions would allow a tailored deconstruction approach to increase overall process efficiency. Air classification can be leveraged for such refining, however, fundamental characterization and understanding of the particle properties that underly the physics of air classification are only modestly documented. Here, we determine fundamental particle properties including mass-to-area ratio, drag coefficient, and partition velocity that describe how anatomical tissues of corn stover behave during air classification. In this work, mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm 2 for cob to 0.04 mg/mm 2 for leaf. Drag coefficients of longer, fibrous materials (i.e., rind, husk, and sheath) are shown to correlate with particle area (p-value < 0.001) whereas granular tissues (i.e., cob, pith, and leaf) correlate better with mass-to-area ratio (p-values < 0.001). When compared to experimental observations, a simulated two-stage air classification and size reduction scenario predicts the overall partitioning of anatomical tissues within 15% for pith, husk, rind, and cob tissues. The model predicts an air-classified fraction preferentially enriched in cob (purity = 20%), rind (purity = 74%), and pith (purity = 4.5%) with a mass yield of 47%. Empirical relations for these properties can be used to predict the partitioning of corn stover during air classification based on anatomical type and size.