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Predictive models enhance feedstock quality of corn stover via air classification

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Abstract

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. Mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm2 for cob to 0.04 mg/mm2 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.

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Data availability

Raw data for area and mass of corn stover particles are provided with this manuscript.

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Funding

This material is based upon work supported by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) Bioenergy Technologies Office (BETO) and FOA-0002029 under the Award Number DE-EE0008907 “Enhanced Feedstock Characterization and Modeling to Facilitate Optimal Preprocessing and Deconstruction of Corn Stover.”

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Authors and Affiliations

  1. Department of Chemical and Biological Engineering, Montana State University, 306 Cobleigh Hall, PO Box 173920, Bozeman, MT, 59717-3920, USA

    Dylan S. Cousins, William G. Otto, Kristian P. Pedersen & David B. Hodge

  2. Idaho National Laboratory, Idaho Falls, ID, USA

    Asif Hasan Rony, Sergio Hernandez, Jeffrey A. Lacey & John E. Aston

  3. Division of Sustainable Process Engineering, Luleå University of Technology, Luleå, Sweden

    David B. Hodge

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  1. Dylan S. Cousins
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  2. Asif Hasan Rony
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Contributions

DSC developed the theoretical framework, designed and carried out the experimental work, and drafted the manuscript. AHR carried out experimental work in air classification. WGO assisted with experimental work sorting anatomical fractions of corn stover. KPP carried out experimental work in sieving and manual fractionation. SH carried out experimental work in material production. JAL helped conceive of the framework and provided insight into the experiments. JEA coordinated material delivery and helped conceive of the theoretical framework. DBH oversaw the project, helped conceive the theoretical framework, provided guidance on manuscript storyboarding, and edited the manuscript.

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Correspondence to David B. Hodge.

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Cousins, D.S., Rony, A.H., Otto, W.G. et al. Predictive models enhance feedstock quality of corn stover via air classification. Biomass Conv. Bioref. (2022). https://doi.org/10.1007/s13399-022-03307-1

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