(625g) Predicting Bioconversion from Physical and Chemical Characteristics of Single and Blended Lignocellulosic Biomass | AIChE

(625g) Predicting Bioconversion from Physical and Chemical Characteristics of Single and Blended Lignocellulosic Biomass


Hoover, A. - Presenter, Idaho National Laboratory
Stevens, D. - Presenter, Idaho National Laboratory
Ray, A. E. - Presenter, Idaho National Laboratory
Park, S. - Presenter, North Carolina state university
Hoeger, I. - Presenter, North Carolina State University
Emerson, R. - Presenter, Idaho National Laboratory
Morgan, S. - Presenter, Idaho National Laboratory
Gresham, G. L. - Presenter, Idaho National Laboratory

Developing biomass blends is a new concept that allows incorporation of low-cost feedstocks into the bioenergy supply chain to decrease overall cost of feedstocks; however, quality of low-cost feedstocks can vary. Chemical composition (e.g., glucan, xylan, lignin), surface area, and elemental ash are examples of key chemical and physical characteristics of biomass that impact conversion processes. The ability to quickly and accurately determine these key quality attributes and convertibility of single biomass feedstocks (e.g., not blended) for biochemical conversion processes is important for understanding how blended feedstocks will perform and to determine value of biomass on the basis of quality in addition to quantity. This project aims to develop a conversion model using single feedstocks to predict convertibility of dilute-acid pretreated biomass from physical and chemical characteristics of biomass and further apply the model to blended feedstocks. Feedstocks will include corn stover, switchgrass, Miscanthus, lawn clippings, sorghum, wheat straw, non-recyclable paper, and blends of these materials. Their physical and chemical characteristics, such as composition, surface area, porosity, cellulose crystalline structure, and elemental ash, are measured and used to predict the corresponding sugar yields from a bench-scale, dilute-acid pretreatment and enzymatic hydrolysis. The model generated will be used to (1) develop blending strategies, (2) determine feedstock characteristics that are most important for predicting conversion, (3) make correlations between chemical and physical characteristics and conversion performance of biomass, and (4) begin to select the most critical techniques necessary to measure quality in the logistical process.