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In this study, the results from laboratory measurements of the devolatilization kinetics of switchgrass in a rapidly heated fixed bed reactor flushed with argon and operated at constant temperatures between 600 and 800°C was reported. Results indicate that switchgrass decomposes in two sequential stages during pyrolysis: stage I involves the evaporation and devolatilization of water and extractives and stage II involves that of hemicellulose, cellulose, and lignin. The estimated global activation energy for stage II increased from 52.80 to 59.39kJ/mol as the reactor temperature was increased from 600 to 800°C. The maximum conversion of carbon, hydrogen, oxygen, sulfur, and nitrogen ranged from 0.68 to 0.70, 0.90 to 0.95, 0.88 to 0.91, 0.70 to 0.80, and 0.55 to 0.66, respectively. The retention of alkali and alkaline earth metal (AAEM) species in the solid char after complete pyrolysis was significantly higher than in the original feed, indicating the importance of AAEM species in subsequent char processing.

The aim of this work was to evaluate the efficiency of an ionic liquid (IL) 1-butyl-3-methylimidazolium acetate ([C4mim][OAc]) pretreatment (110°C for 30min) in comparison to high severity autohydrolysis pretreatment in terms of delignification, cellulose crystallinity and enzymatic digestibility. The increase in severity of autohydrolysis pretreatment had positive effect on glucan digestibility, but was limited by the crystallinity of cellulose. [C4mim][OAc] pretreated sugarcane bagasse exhibited a substantial decrease in lignin content, reduced cellulose crystallinity, and enhanced glucan and xylan digestibility. Glucan and xylan digestibility was determined as 97.4% and 98.6% from [C4mim][OAc] pretreated bagasse, and 62.1% and 57.5% from the bagasse autohydrolyzed at 205°C for 6min, respectively. The results indicated the improved digestibility and hydrolysis rates after [C4mim][OAc] pretreatment when compared against a comparable autohydrolyzed biomass.

A new approach to hydrogen production using an integrated pyrolysis–microbial electrolysis process is described. The aqueous stream generated during pyrolysis of switchgrass was used as a substrate for hydrogen production in a microbial electrolysis cell, achieving a maximum hydrogen production rate of 4.3 L H2/L anode-day at a loading of 10 g COD/L-anode-day. Hydrogen yields ranged from 50 ± 3.2% to 76 ± 0.5% while anode Coulombic efficiency ranged from 54 ± 6.5% to 96 ± 0.21%, respectively. Significant conversion of furfural, organic acids and phenolic molecules was observed under both batch and continuous conditions. The electrical and overall energy efficiency ranged from 149–175% and 48–63%, respectively. The results demonstrate the potential of the pyrolysis–microbial electrolysis process as a sustainable and efficient route for production of renewable hydrogen with significant implications for hydrocarbon production from biomass.

Fractionation of lignocellulosic biomass is an attractive solution to develop an economically viable biorefinery by providing a saccharide fraction to produce fuels and a lignin stream that can be converted into high value products such as carbon fibers. In this study, the analysis of ionic liquid-activated biomass demonstrates that in addition of decreasing crystallinity, the selected ILs (1-butyl-3-methylimidazolium acetate, 1-butyl-3-methylimidazolium chloride and 1-ethyl-3-methylimidazolium acetate) deacetylate Yellow poplar under mild conditions (dissolution at 60–80 °C), and lower the degradation temperature of each biomass polymeric component, thereby reducing the recalcitrance of biomass. Among the three tested ILs, 1-ethyl-3-methylimidazolium acetate performed the best, providing a strong linear relationship between the level of deacetylation and the rate of enzymatic saccharification for Yellow poplar.

Rapid methods for the characterization of biomass for energy purpose utilization are fundamental. In this work, near infrared spectroscopy is used to measure ash and char content of various types of biomass. Very strong models were developed, independently of the type of biomass, to predict ash and char content by near infrared spectroscopy and multivariate analysis. Several statistical approaches such as principal component analysis (PCA), orthogonal signal correction (OSC) treated PCA and partial least squares (PLS), Kernel PCA and PLS were tested in order to find the best method to deal with near infrared data to classify and predict these biomass characteristics. The model with the highest coefficient of correlation and the lowest RMSEP was obtained with OSC-treated Kernel PLS method.