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Multiscale Characterization of Lignocellulosic Biomass Variability and Its Implications to Preprocessing and Conversion: a Case Study for Corn Stover

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.
- Los Alamos National Laboratory United States
- Lawrence Berkeley National Laboratory United States
- Lawrence Berkeley National Laboratory United States
- University of California System United States
- Sandia National Laboratories United States
Multiscale characterization, 670, Biomass variability, Environmental Science and Management, Inorganic speciation, Emergent properties, Chemical Engineering, k-means clustering, 630, Analytical Chemistry, Engineering, Chemical engineering, Material attributes, Chemical Sciences, Corn stover, Analytical chemistry
Multiscale characterization, 670, Biomass variability, Environmental Science and Management, Inorganic speciation, Emergent properties, Chemical Engineering, k-means clustering, 630, Analytical Chemistry, Engineering, Chemical engineering, Material attributes, Chemical Sciences, Corn stover, Analytical chemistry
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).37 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
