Location: Natural Resource Management ResearchTitle: Moisture sorption kinetics of switchgrass, big bluestem, and bromegrass biomass Author
Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/27/2014
Publication Date: 8/26/2014
Publication URL: http://handle.nal.usda.gov/10113/59544
Citation: Yu, M., Igathinathane, C., Hendrickson, J.R., Sanderson, M.A. 2014. Moisture sorption kinetics of switchgrass, big bluestem, and bromegrass biomass. Transactions of the ASABE. 57(4):1219-1230. Interpretive Summary: Biomass moisture status is the most influential factor of biomass storage that affects biomass quality and conversion process. In this research, we determined the moisture sorption characteristics of switchgrass, big bluestem, and bromegrass (economically important biomass feedstocks on the northern Great Plains) over a range of temperatures and used the information to develop prediction models for moisture sorption. Our results demonstrated that bromegrass had the initial highest moisture absorption rate, highest water sorption capacity, and took longer to reach equilibrium moisture content than big bluestem or switchgrass. The moisture sorption characteristics of all grasses varied with temperature with greater moisture sorption at higher temperatures. Comparison of several mathematical models indicated that all were suited to predicting moisture sorption characteristics of the grasses. The prediction models can be coupled with visual graphics to rapidly estimate moisture and storage characteristics of biomass feedstocks. The results from this study contribute baseline data for estimating biomass storage life and quality of biomass in storage.
Technical Abstract: Moisture status in biomass is the most influential factor of biomass storage, and hydration kinetics control the dynamic moisture condition of the biomass, thus affecting biomass storage and processing operations and final utilization applications. Moisture hydration characteristics of switchgrass, big bluestem, and bromegrass, biomass feedstocks in Midwest region of US, were studied to determine the moisture absorption kinetics, mathematically model hydration process using standard models, and evaluate the effect of temperature in moisture absorption. Experimental moisture hydration characteristics were conducted at temperatures of 20°C, 40°C, and 60°C and a fixed high relative humidity of 95% using an environmental control chamber. Standard moisture hydration kinetics models, such as the exponential, Page, and Peleg models were used to analyze the experimental hydration characteristics of the biomass. Results showed that pseudo equilibrium moisture contents were 19.44%, 21.19%, and 24.78% at 20°C; and 20.97%, 24.51% and 32.60% at 60°C for switchgrass, big bluestem and bromegrass, respectively. Bromegrass had higher sorption capacity than switchgrass and big bluestem. From the experimental moisture absorption data, at 20°C, 58.4%, 56.1% and 57.5% moisture sorption took place within 1 h (85.3%, 79.1% and 81.6% in 5 h) for switchgrass, big bluestem, and bromegrass, respectively. Sorption rates were reduced sharply in the initial period (73.0%, 86.3% and 87.1% in 1 h at 20°C for switchgrass, big bluestem and bromegrass, respectively). Moisture sorption rates were reduced greatly during first hour (>73%) and plateaued thereafter. In general, increases in temperature increased the sorption rates for all biomass species. Bromegrass absorbed moisture most rapidly followed by big bluestem and switchgrass, and increased temperatures enhanced the absorption rates for all biomass feedstocks. Both the Page and Peleg models (R2 > 0.95) effectively described the observed sorption characteristics for the biomass species. The Arrhenius equation adequately described the temperature dependence of the model parameters (R2 > 0.94). Based on this study, the Peleg model in combination with the Arrhenius equation is recommended for moisture sorption predictions. We developed easy-to-use nomograms and combined them with prediction models for rapid moisture predictions for the studied feedstocks.