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Perennial grasses and corn stalks can supply abundant lignocellulosic feedstock in the northern Great Plains of the U.S. There is a need to understand the mechanical properties of these crops for better handling and processing of biomass feedstocks in bioprocessing industries. The objectives of this research included determination of tensile and shear stresses of big bluestem, corn stalk, intermediate wheatgrass, and switchgrass stems and measurement of specific tensile and shear energies during tensile and shear failure. A high-capacity MTI-100K universal testing machine equipped with adapted tensile clamps and a specially designed and fabricated double-shear device were used for tensile and shear testing. Ultimate shear stresses were not statistically different for big bluestem, corn stalk, and intermediate wheatgrass, with values of 7.33, 8.53 and 6.23 MPa, respectively, which were less than switchgrass at 13.39 MPa (p < 0.05). Corn stalk had the greatest ultimate tensile stress of 69.30 MPa, followed by switchgrass, big bluestem, and intermediate wheatgrass. The ultimate tensile stress and shear stress were statistically different (p < 0.05) from each other for all four feedstocks. Both ultimate shear energy and specific shear energy for corn stalk were statistically different (p < 0.05) from big bluestem, intermediate wheatgrass, and switchgrass. The evaluated shear to tensile stress ratios, expressed as percentages, were 29.3%, 12.3%, 30.5%, and 31.9%, whereas the shear to tensile energy ratios were greater, with values of 42.2%, 67.2%, 61.2%, 50.4% for big bluestem, corn stalk, intermediate wheatgrass, and switchgrass, respectively. Based on these results, shear-dominant size-reduction devices (e.g., knife mills and chippers) should be more energy efficient, and this energy efficiency should be taken advantage of when designing size-reduction devices. Further research efforts are necessary to determine the effect of moisture, crop variety, and maturity stage, as these factors are known to affect the mechanical characteristics of biomass feedstocks. [ABSTRACT FROM AUTHOR]

Moisture content of biomass is the most influential factor in biomass storage. Moisture sorption kinetics control the dynamic moisture condition of the biomass, thus affecting biomass storage, processing operations, and final utilization applications. Moisture sorption characteristics of switchgrass, big bluestem, and bromegrass, potential biomass feedstocks for the Northern Great Plains of the U.S., were studied. Study objectives were to determine the moisture sorption kinetics, mathematically model the sorption process using standard models, and evaluate the effect of temperature on moisture sorption. Moisture sorption experiments were conducted at temperatures of 20°C, 40°C, and 60°C and a fixed high relative humidity of 95% using a controlled-environment chamber. Standard moisture sorption kinetics models (exponential, Page, and Peleg) were used to analyze the experimental sorption characteristics of the feedstocks. Bromegrass had the highest moisture sorption rates and final moisture contents, followed by big bluestem and switchgrass. On average, at 20°C, 50% of moisture sorption completion occurred at 1.5, 1.9, and 1.7 h and 90% completion occurred at about 8.5, 13.4, and 12.8 h for switchgrass, big bluestem, and bromegrass, respectively. For the temperatures studied, on average 41% ±3%, 40% ±5%, and 39% ±1% of moisture sorption completion occurred in 1 h and 82% ±2%, 76% ±4%, and 73% ±1% completion occurred in 5 h for switchgrass, big bluestem, and bromegrass, respectively. Moisture sorption rates decreased very sharply during the first hour (≥78%) from their initial values and quickly plateaued thereafter. Increase in temperature increased the moisture sorption rates for all the biomass types tested. Both the Page and Peleg models effectively described the observed sorption characteristics for the selected biomass types (R² ≥ 0.96). The Arrhenius equation adequately described the temperature dependence of the model parameters (0.77 ≤ R² ≤ 1.00). Based on this study, the Peleg model in combination with the Arrhenius equation is recommended for moisture sorption predictions. Fitted moisture sorption kinetics models, developed nomograms, and combined prediction equations (R² ≥ 0.83) form baseline data essential for storage of the selected biomass types and various handling, conditioning, and processing operations. [ABSTRACT FROM AUTHOR]