18-12: Optimizing parameters for enhanced cellulosic biomass conversion through multi-staged continuous simultaneous saccharification and fermentation (cSSF)

Tuesday, May 1, 2012
Napoleon Ballroom C-D, 3rd fl (Sheraton New Orleans)
YI Jin, Department of Chemical and Environmental Engineering, Bourns College of Engineering, Center for Environmental Research and Technology, University of California, Riverside, Riverside, CA, Rajeev Kumar, University of California, Center for Environmental Research and Technology, Riverside, CA and Charles Wyman, Center for Environmental Research and Technology Department of Chemical and Environmental Engineering Bourns College of Engineer, University of California at Riverside, Riverside, CA
Although many economic studies assume continuous simultaneous saccharification and fermentation (cSSF) is the economical configuration for commercial applications to achieve higher ethanol concentration and productivity, little experience is available to guide process design or optimization.  Furthermore, improvements must be made in continuous simultaneous saccharification and fermentation (cSSF) to overcome rate limitations of enzymatic hydrolysis at low enzyme loadings and high solid concentrations of commercial relevance and take better advantage of enzymes in enhancing fermentation performance. Therefore, this study focused on developing optimal operating strategies for multi-stage cSSF based on both experiments and kinetic models. Trade-offs among residence times, enzyme loadings, and organism washout were considered for up to three continuous SSF fermentors in series. Comparisons were made between batch and continuous processing to understand how end product inhibition and other characteristics differ and their effects on performance. Because trade-offs among enzyme loadings and ethanol yields are particularly vital to lowering the cost of cellulosic ethanol production, staged introduction of enzymes into the continuous SSF train was compared to results from batch and fed-batch operations, and enzyme loadings and residence times were identified to give the highest enzyme effectiveness.
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