Projeto: Mathematical modeling of auto-hydrolysis and organosolv applied to the pretreatment of lignocellulosic biomass

ABSTRACT

Bioethanol, a biofuel that has been advocated as a sustainable option to tackle the problems associated with rising crude oil prices, global warming and diminishing petroleum reserves. It provides a number of environmental advantages over conventional fossil fuels used in the transportation sector—most notably a reduction in greenhouse gas emissions. Second-generation bioethanol is produced from lignocellulosic feedstock typically involving three steps:

(i) biomass pretreatment in order to separate the three main components (lignin, hemicellulose, cellulose),

(ii) conversion of extracted cellulose into sugars, typically through enzymatic hydrolysis,

(iii) microbial fermentation of sugars to obtain ethanol. However, the natural recalcitrance of biomass (resistance of lignin to breakdown) affects the hydrolysis enzymatic (second step) in the production of bioethanol, which has led to the need of improving pretreatments. Recent trends in green biotechnology suggest the potential (industrial) application of ligninolytic enzymes, as well as autohydrolysis and organosolv in pretreatments of lignocellulosic biomass.

Other problem affecting enzymatic hydrolysis is the inherent high crystallinity of cellulose that decreases the susceptibility to enzymatic attack. Dissolution (or amorphisation) of cellulose would increases the production of oligosaccharides, and accordingly the yield of sugars for bioethanol production. In order to predict the industrially feasible conditions and get molecular level insight of these issues, our study is addressed by dynamic molecular (MD) and quantum methods (QM). To that end, as a first stage of investigation, simulations of the autohydrolysis and organosolv pretreatments of lignocellulosic biomass, and solubilization of cellulose are being undertaken. In future researches, biochemistry reactions of enzymatic attack on lignin and cellulose will be carried out using hybrid methods MD-QM in order to optimize enzymatic cocktails that would improve the yield of bioethanol.