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Predictive models e...
Predictive models enhance feedstock quality of corn stover via air classification
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- Cousins, Dylan S. (författare)
- Department of Chemical and Biological Engineering, Montana State University, 306 Cobleigh Hall, PO Box 173920, Bozeman, MT, 59717-3920, USA
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- Rony, Asif Hasan (författare)
- Idaho National Laboratory, Idaho Falls, ID, USA
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- Otto, William G. (författare)
- Department of Chemical and Biological Engineering, Montana State University, 306 Cobleigh Hall, PO Box 173920, Bozeman, MT, 59717-3920, USA
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- Pedersen, Kristian P. (författare)
- Department of Chemical and Biological Engineering, Montana State University, 306 Cobleigh Hall, PO Box 173920, Bozeman, MT, 59717-3920, USA
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- Hernandez, Sergio (författare)
- Idaho National Laboratory, Idaho Falls, ID, USA
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- Lacey, Jeffrey A. (författare)
- Idaho National Laboratory, Idaho Falls, ID, USA
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- Aston, John E. (författare)
- Idaho National Laboratory, Idaho Falls, ID, USA
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- Hodge, David B. (författare)
- Luleå tekniska universitet,Kemiteknik,Department of Chemical and Biological Engineering, Montana State University, 306 Cobleigh Hall, PO Box 173920, Bozeman, MT, 59717-3920, USA
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(creator_code:org_t)
- 2022-10-03
- 2024
- Engelska.
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Ingår i: Biomass Conversion and Biorefinery. - : Springer Nature. - 2190-6815 .- 2190-6823. ; 14:13, s. 13833-13845
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
Nyckelord
- Air classification
- Feedstock enhancement
- Feedstock enhancement
- Biorefnery
- Particle image analysis
- Comminution
- Biomass separation
- Biokemisk processteknik
- Biochemical Process Engineering
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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