MOISTURE SORPTION KINETICS OF SWITCHGRASS, BIG BLUESTEM, AND BROMEGRASS BIOMASS.

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]