Preprocessing with air classification, followed by a hybrid biochemical/thermochemical conversion scheme, was utilized to improve the quality of short rotation woody coppice (SRWC) for biofuels production. Air classification improved sugar release during enzymatic hydrolysis by 6-12% for poplar and willow coppice respectively. Total theoretical sugar release for these hardwood coppices was ∼70%, which suggests that they could be utilized for biochemical conversion. Improved sugar yields after air classification were tied to compositional changes of reduced ash and extractives which can neutralize dilute acid pretreatment and inhibit fermentation. However, air classification was shown to have little to no effect on pyrolytic thermochemical conversion as it removed material without returning a significant improvement in liquid yield. It was also shown that pyrolysis of biochemical conversion lignin rich residue gives liquid yields comparable to whole tree (without any fractionation) pyrolysis, with a higher quality oil that has ∼60% reduced total acid number. Using this combined biochemical/thermochemical conversion strategy can improve yields of fermentable sugars and pyrolysis liquid above 80%, instead of the 60% yield of sugars or bio-oil when using a single conversion strategy. Overall, it has been shown that preprocessing and hybrid conversion pathways are a viable strategy for maximizing biorefinery viability.

This paper examines the efficacy of ionic liquid (IL) pretreatment on seven different commercially harvested biomass types: corn stover, miscanthus, pine, sorghum, sugarcane bagasse, switchgrass, and wheat straw in an effort to improve the production of renewable fuels and chemicals from biomass derived sugars. Initial experiments screened the pretreatment of lodgepole pine, a particularly recalcitrant biomass feedstock, with nine different imidazolium based ionic liquids. After screening, one hydrophilic and one hydrophobic ionic liquid was selected for pretreatment tests on six commercially harvested biomasses. Ultimately, the hydrophilic ionic liquid functioned better for biomass pretreatment than the hydrophobic ionic liquid. These results were then compared to a traditional dilute acid pretreatment to examine the relative effectiveness of ionic liquid pretreatment across a variety of biomass and ionic liquid types. Total theoretical sugar yields after IL pretreatment varied widely by IL and biomass type and ranged from 4.9 to 90.2%. Dilute acid pretreatment showed consistent sugar yields for herbaceous material (from 71.4 to 80.8%) but low yield for lodgepole pine (22.8%). Overall, ILs showed the potential to reach slightly higher sugar yields than dilute acid and were particularly effective for woody feedstocks. More importantly, the sugar release kinetics for IL pretreatment were three times faster than dilute acid and gave maximum sugar yields after about 24 h. Additional characterization of IL treated materials included scanning electron microscopy (SEM), x-ray diffraction (XRD), and compositional analysis. SEM and XRD showed qualitative and quantitative reductions in cellulose crystallinity (respectively) that correlated well to improved sugar release during enzymatic hydrolysis for hydrophilic ionic liquids. However, reductions in crystallinity associated with hydrophobic ionic liquids resulted in lower sugar release during enzymatic hydrolysis. Compositional analysis generally showed increased sugars content for hydrophilic ILs and increased lignin content for hydrophobic ILs.

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Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.