Microwave Pretreatment of Switchgrass for Bioethanol Production

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Title: Microwave Pretreatment of Switchgrass for Bioethanol Production
Author: Keshwani, Deepak Radhakrishin
Advisors: Ratna R. Sharma-Shivappa, Committee Member
Lucian A. Lucia, Committee Member
Jay J. Cheng, Committee Chair
Andy Hale, Committee Member
Sharon R. Lubkin, Committee Member
Abstract: Lignocellulosic materials are promising alternative feedstocks for bioethanol production. These materials include agricultural residues, cellulosic waste such as newsprint and office paper, logging residues, and herbaceous and woody crops. However, the recalcitrant nature of lignocellulosic biomass necessitates a pretreatment step to improve the yield of fermentable sugars. The overall goal of this dissertation is to expand the current state of knowledge on microwave-based pretreatment of lignocellulosic biomass. Existing research on bioenergy and value-added applications of switchgrass is reviewed in Chapter 2. Switchgrass is an herbaceous energy crop native to North America and has high biomass productivity, potentially low requirements for agricultural inputs and positive environmental impacts. Based on results from test plots, yields in excess of 20 Mg/ha have been reported. Environmental benefits associated with switchgrass include the potential for carbon sequestration, nutrient recovery from run-off, soil remediation and provision of habitats for grassland birds. Published research on pretreatment of switchgrass reported glucose yields ranging from 70-90% and xylose yields ranging from 70-100% after hydrolysis and ethanol yields ranging from 72-92% after fermentation. Other potential value-added uses of switchgrass include gasification, bio-oil production, newsprint production and fiber reinforcement in thermoplastic composites. Research on microwave-based pretreatment of switchgrass and coastal bermudagrass is presented in Chapter 3. Pretreatments were carried out by immersing the biomass in dilute chemical reagents and exposing the slurry to microwave radiation at 250 watts for residence times ranging from 5 to 20 minutes. Preliminary experiments identified alkalis as suitable chemical reagents for microwave-based pretreatment. An evaluation of different alkalis identified sodium hydroxide as the most effective alkali reagent. Under optimum pretreatment conditions, 82% glucose and 63% xylose yields were achieved for switchgrass, and 87% glucose and 59% xylose yields were achieved for coastal bermudagrass following enzymatic hydrolysis of the pretreated biomass. The optimum enzyme loadings were 15 FPU/g and 20 CBU/g for switchgrass and 10 FPU/g and 20 CBU/g for coastal bermudagrass. Dielectric properties for dilute sodium hydroxide solutions were measured and compared to solid loss, lignin reduction and reducing sugar levels in hydrolyzates. Results indicate that the dielectric loss tangent of alkali solutions is a potential indicator of the severity of microwave-based pretreatments. Modeling of pretreatment processes can be a valuable tool in process simulations of bioethanol production from lignocellulosic biomass. Chapter 4 discusses three different approaches that were used to model delignification and carbohydrate loss during microwave-based pretreatment of switchgrass: statistical linear regression modeling, kinetic modeling using a time-dependent rate coefficient, and a Mamdani-type fuzzy inference system. The dielectric loss tangent of the alkali reagent and pretreatment time were used as predictors in all models. The statistical linear regression model for delignification gave comparable root mean square error (RMSE) values for training and testing data and predictions were approximately within 1% of experimental values. The kinetic model for delignification and xylan loss gave comparable RMSE values for training and testing data sets and predictions were approximately within 2% of experimental values. The kinetic model for cellulose loss was not as effective and predictions were only within 5-7% of experimental values. The time-dependent rate coefficients of the kinetic models calculated from experimental data were consistent with the heterogeneity (or lack thereof) of individual biomass components. The Mamdani-type fuzzy inference system was shown to be an effective means to model pretreatment processes and gave the most accurate predictions (<3%) for cellulose loss.
Date: 2009-02-23
Degree: PhD
Discipline: Biological and Agricultural Engineering
URI: http://www.lib.ncsu.edu/resolver/1840.16/5754

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