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Description:
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Lignocellulosic biomass is one of the most valuable alternative energy sources
because it is renewable , widely available , and environmentally friendly . Unfortunately ,
enzymatic hydrolysis of biomass has been shown to be a limiting factor in the
conversion of biomass to chemicals and fuels . This limitation is due to inherent
structural features (i .e . , acetyl content , lignin content , crystallinity , surface area , particle
size , and pore volume ) of biomass . These structural features are barriers that prevent
complete hydrolysis ; therefore , pretreatment techniques are necessary to render biomass
highly digestible .
The ability to predict the biomass reactivity based solely on its structural features
would be of monumental importance . Unfortunately , no study to date can predict with
certainty the digestibility of pretreated biomass . A concerted effort with Auburn
University and Michigan State University has been undertaken to study hydrolysis
mechanisms on a fundamental level . Predicting enzymatic hydrolysis based solely on
structural features (lignin content , acetyl content , and crystallinity index ) would be a
major breakthrough in understanding enzymatic digestibility .
It was proposed to develop a fundamental understanding of the structural features
that affect the enzymatic reactivity of biomass . The effects of acetyl content ,
crystallinity index (CrI ) , and lignin content on the digestibility of biomass (i .e . , poplar
wood , bagasse , corn stover , and rice straw ) were explored .
In this fundamental study , 147 poplar wood model samples with a broad
spectrum of acetyl content , CrI , and lignin were subjected to enzymatic hydrolysis to
determine digestibility . Correlations between acetyl , lignin , and CrI and linear hydrolysis profiles were developed with a neural network model in Matlabà ® . The
average difference between experimentally measured and network -predicted data were
à ±12 % , à ±18 % , and à ±27 % for 1 - , 6 - , and 72 -h total sugar conversions , respectively . The
neural network models that included cellulose crystallinity as an independent variable
performed better compared to networks with biomass crystallinity , thereby indicating
that cellulose crystallinity is more effective at predicting enzymatic hydrolysis than
biomass crystallinity . Additionally , including glucan slope in the 6 -h and 72 -h xylan
slope networks and glucan intercept in the 6 -h and 72 -h xylan intercept networks
improved their predictive ability , thereby suggesting glucan removal affects later -stage
xylan digestibility . |