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

Abstract

Background: 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.

Results: 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 consistently 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.

Conclusions: 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. 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.

Keywords: Biofuels; Compositional analysis; MESP; NIST RM 8491; Sugarcane bagasse; Variability.

Fig. 1

Compositional data from short-term round-robin set (RR) plotted by analyst. Each batch was run by a different analyst with 7–10 replicates of the NIST RM 8491 material. This material was extracted in bulk, and all the data were collected from one chromatography system in order to minimize variability. The gray band in the background shows the two times the grand standard deviation centered on the grand average (denoted by the central line) for the entire RR dataset. Analysts 5 and 9 did not run this experiment. The carbohydrate data for analyst 4 was an outlier and not included here, therefore a total component closure cannot be calculated

Fig. 2

Control charts of compositional data for the long-term extractives-free dataset (bulk-extracted NIST RM 8491 sugarcane bagasse composition) plotted chronologically. Samples in this set were analyzed along with process intermediate samples. The central green line denotes the average value, while the dashed red lines show two times the standard deviation and solid red lines show three times the standard deviation

Fig. 3

Control charts of compositional data for the long-term individually extracted biomass dataset (individually extracted NIST RM 8491 sugarcane bagasse composition) plotted chronologically. This set was analyzed along with feedstock samples. The central green line denotes the average value, while the dashed red lines show two times the standard deviation and solid red lines show three times the standard deviation

Fig. 4

Comparison of standard deviations calculated on different datasets. A shows comparisons of short- and long-term variability between pooled, regular, and calculated based on average sugar recovery values of standard deviations. B shows differences in regular standard deviation between short- and long-term data sets and includes previously analyzed data. Lower case letters indicate significant differences using F test

Fig. 5

Box plot of major components presented by analyst on LT-EF sample group. The gray band in the background shows the two times the standard deviation centered on the mean (denoted by the central line) value for the entire set. Analyst 7 did not run this sample type

Fig. 6

Histogram of MESP values calculated based on 2011 biochemical design case model using complete LT-IE bagasse compositions, which shows variation due to feedstock composition variability. Average MESP = $2.71 per gallon with a standard deviation of $0.03 per gallon