1. Multiscale Characterization of Lignocellulosic Biomass Variability and Its Implications to Preprocessing and Conversion: a Case Study for Corn Stover
- Author
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Ray, Allison E, Williams, C Luke, Hoover, Amber N, Li, Chenlin, Sale, Kenneth L, Emerson, Rachel M, Klinger, Jordan, Oksen, Ethan, Narani, Akash, Yan, Jipeng, Beavers, Christine M, Tanjore, Deepti, Yunes, Manal, Bose, Elizabeth, Leal, Juan H, Bowen, Julie L, Wolfrum, Edward J, Resch, Michael G, Semelsberger, Troy A, and Donohoe, Bryon S
- Subjects
Chemical Engineering ,Analytical Chemistry ,Engineering ,Chemical Sciences ,Biomass variability ,Multiscale characterization ,Corn stover ,k-means clustering ,Inorganic speciation ,Material attributes ,Emergent properties ,Environmental Science and Management ,Analytical chemistry ,Chemical engineering - Abstract
Feedstock variability that originates from biomass production and field conditions propagates through the value chain, posing a significant challenge to the emerging biorefinery industry. Variability in feedstock properties impacts feeding, handling, equipment operations, and conversion performance. Feedstock quality attributes, and their variations, are often overlooked in assessing feedstock value and utilization for conversion to fuels, chemicals, and products. This study developed and employed a multiscale analytical characterization approach coupled with data analytic methods to better understand the sources and distribution of feedstock quality variability through evaluation of 24 corn stover bales collected in 4 counties of Iowa. In total, 216 core samples were generated by sampling nine positions on each bale using a reliable bale coring process. The samples were characterized for a broad suite of physicochemical properties ranging across field and bale, macro, micro, and molecular scales. Results demonstrated that feedstock quality attributes can vary at all spatial scales and that multiple sources of variability must be considered in order to establish and manage biomass quality for conversion processes.
- Published
- 2020