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Item Methods for Ethanol Production from the Enzymatic Hydrolysis and Fermentation of Sugar Beet Pulp(North Dakota State University, 2010) Rorick, Rachel ElizabethSugar beet pulp (SBP), the residue remaining after sucrose extraction, is currently sold as an animal feed. Humans cannot digest the cellulose in the pulp unlike ruminant animals. The pulp is primarily comprised of cellulose, hemicellulose, and pectin which can be hydrolyzed with commercial enzymes into fermentable sugars such as, glucose, arabinose, galacturonic acid, xylose, and galactose. These sugars can be fermented to produce ethanol. This research tested the variation of several enzymes, enzyme loading rates, solids loading rates, and fermenting organisms to increase ethanol yields from sugar beet pulp. Several commercial enzymes (cellulases, hemicellulases, pectinases, and proteases) were tested to determine impact on SBP hydrolysis. Two commercial enzyme preparations (Viscozyme and Pectinex) were compared. Viscozyme produced the highest sugar yields because of increased cellulose hydrolysis, while Pectinex showed less cellulase activity. All enzyme treatments resulted in similar hemicellulose and pectin hydrolysis. Pretreatment with proteases reduced sugar yields from hydrolysis by 10-30% compared to hydrolysis without pretreatment. Escherichia coli K011, a genetically modified organism (GMO), and Saccharomyces cerevisiae were used to ferment SBP hydrolyzate to increase ethanol yields (g EtOH/g SBP) and concentrations (g/L). In the "Parallel" fermentation, pectinase was used to solubilize pectin and hemicellulose. After separation, the liquid stream was fermented with E. coli K011 and the high-cellulose solid fraction was fermented using S. cerevisiae and additional cellulase enzymes (Celluclast and Novozyme 188). The "Parallel" method initially produced under 0.15 g EtOH/g SBP but was improved with pH regulation to yield 0.23 g EtOH/g SBP. The separation method limited ethanol production. The ethanol yields from three additional fermentation methods ("E. coli K011 Only", "Serial", and "Reverse Serial") were compared. The "E. coli K011 Only" method was the baseline fermentation for comparison of the remaining three fermentation methods. SBP was hydrolyzed with pectinase, cellulase, and cellobiase before fermentation with E. coli K011 to yield 0.192 g ethanol/ g SBP. The total hydrolysis of the SBP limited ethanol production. The "Serial" fermentation began by solubilizing pectin and hemicellulose with pectinases. All of the flask contents were fermented with E. coli K011. The remaining cellulose-rich SBP was then hydrolyzed with cellulases and fermented by S. cerevisiae. Initial ethanol yields were under 0.15 g EtOH/g SBP but improved to 0.238 g EtOH/g SBP. Acetic acid concentrations limited ethanol production by S. cerevisiae. The "Reverse Serial" simultaneous saccharification and fermentation (SSF) started with pectinases, cellulases, cellobiases, and S. cerevisiae. Remaining arabinose and galacturonic acid were fermented with E. coli K011 to produce a peak ethanol yield of 0.299 g EtOH/g SBP. The methods approached and exceeded published results (0.277 g EtOH/g SBP) (Doran and Foster, 2000) to successfully increase ethanol yields. Ethanol concentrations were limited by high SBP moisture content and low solids loading rates.Item Evaluating Standard Wet Chemistry Techniques and NIR Spectroscopic Models for Determining Composition and Potential Ethanol Yields of Multi-Species Herbaceous Bioenergy Crops(North Dakota State University, 2011) Monona, Ewumbua MenyoliHerbaceous perennials represent a considerable portion of potential biomass feedstocks available for the growing bioenergy industry. Their chemical composition and biomass yields, which are important in determining ethanol potential on an area and mass basis, vary with plant variety and type, environment, and management practices. Therefore, a study was conducted to assess the variability of lignin and carbohydrate content, biomass yields, and theoretical ethanol yields on an area basis among different herbaceous perennial species combinations grown in Minot (2008) and Williston (2008, 2009, and 2010), North Dakota (ND). After wet chemistry compositional analysis was done, the carbohydrate contents were used to determine theoretical ethanol potential on a mass basis. Using the dry-matter yield, the theoretical ethanol yield on an area basis was also calculated for these biomass species. Total carbohydrate content for the biomass samples in Williston and Minot varied from 45 to 61% dry basis. Analysis of Variance (ANOVA) at a= 0.05 showed that carbohydrate content varied between years and environments. Also an interaction plot shows that no biomass species had consistently higher or lower carbohydrate content in the different environments. Switchgrass (Panicum vigatum L.) grown as single species or together with other perennial grasses had higher dry-matter yield and theoretical ethanol yield potential in Williston irrigated plots while mixtures containing intermediate or tall wheatgrass species (Thinopyrum spp.) produced better yields in Minot non-irrigated plots. Variability in theoretical ethanol yield on a mass basis (3.7% coefficient of variation (CV) in Williston and 9.7% CV in Minot) was much less than the variability in dry-matter yields (27.5% CV in Williston and 14.8% CV Minot). Therefore, biomass production is much more important than composition in choosing species to grow for ethanol production. Recently, many studies have focused on developing faster methods to determine biomass composition using near infrared (NIR) spectroscopy. Other NIR models have been developed on single biomass feedstocks but a broad-based model for mixed herbaceous perennials is yet to be developed. Therefore, NIR calibration models for lignin, glucan, and xylan were developed with 65 mixed herbaceous perennial species using a DA 7200 NIR spectrometer (950 - 1,650 nm) and GRAMS statistical software. The models for lignin and xylan had R(2) values of 0.844 and 0.872, respectively, upon validation and are classified as good for quality assurance purposes while glucan model had an R(2) of 0.81 which is considered sufficient for screening. The R(2) and the root mean square error of prediction (RMSEP) results showed that it is possible to develop calibration models to predict chemical composition for mixed perennial biomass when compared with results for models developed for single feedstock by Wolfrum and Sluiter (2009) and Liu et al. (2010). Studying the variability in predicting constituents using NIR spectroscopy over time (hours and days), it was observed that the average CV was between 1.4 to 1.6%. The average CV due to repacking (presentation) alone was 1.3%. The CVs for NIR predictions ranged between 1.4 to 5.7% while for wet chemistry ranged between 3.8 to 13.5%; hence, NIR predictions were more precise than wet chemistry analysis.