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Title: Long-term variability in sugarcane bagasse feedstock compositional methods: Sources and magnitude of analytical variability

In an effort to find economical, carbon-neutral transportation fuels, biomass feedstock compositional analysis methods are used to monitor, compare, and improve biofuel conversion processes. These methods are empirical, and the analytical variability seen in the feedstock compositional data propagates into variability in the conversion yields, component balances, mass balances, and ultimately the minimum ethanol selling price (MESP). We report the average composition and standard deviations of 119 individually extracted National Institute of Standards and Technology (NIST) bagasse [Reference Material (RM) 8491] run by seven analysts over 7 years. Two additional datasets, using bulk-extracted bagasse (containing 58 and 291 replicates each), were examined to separate out the effects of batch, analyst, sugar recovery standard calculation method, and extractions from the total analytical variability seen in the individually extracted dataset. We believe this is the world's largest NIST bagasse compositional analysis dataset and it provides unique insight into the long-term analytical variability. Understanding the long-term variability of the feedstock analysis will help determine the minimum difference that can be detected in yield, mass balance, and efficiency calculations. The long-term data show consistent bagasse component values through time and by different analysts. This suggests that the standard compositional analysis methods were performed consistentlymore » and that the bagasse RM itself remained unchanged during this time period. The long-term variability seen here is generally higher than short-term variabilities. It is worth noting that the effect of short-term or long-term feedstock compositional variability on MESP is small, about $0.03 per gallon. The long-term analysis variabilities reported here are plausible minimum values for these methods, though not necessarily average or expected variabilities. We must emphasize the importance of training and good analytical procedures needed to generate this data. As a result, when combined with a robust QA/QC oversight protocol, these empirical methods can be relied upon to generate high-quality data over a long period of time.« less
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
OSTI Identifier:
1330303
Report Number(s):
NREL/JA--5100-66972
Journal ID: ISSN 1754-6834
Grant/Contract Number:
AC36-08GO28308
Type:
Accepted Manuscript
Journal Name:
Biotechnology for Biofuels
Additional Journal Information:
Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 1754-6834
Publisher:
BioMed Central
Research Org:
NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B)
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS compositional analysis; sugarcane bagasse; variability; biofuels; NIST RM 8491; MESP