Lignocellulose is a promising and valuable alternative energy source. Native
lignocellulosic biomass has limited accessibility to cellulase enzyme due to structural
features; therefore, pretreatment is an essential prerequisite to make biomass accessible
and reactive by altering its structural features.
The effects of substrate concentration, addition of cellobiase, enzyme loading,
and structural features on biomass digestibility were explored. The addition of
supplemental cellobiase to the enzyme complex greatly increased the initial rate and
ultimate extent of biomass hydrolysis by converting the strong inhibitor, cellobiose, to
glucose. A low substrate concentration (10 g/L) was employed to prevent end-product
inhibition by cellobiose and glucose. The rate and extent of biomass hydrolysis
significantly depend on enzyme loading and structural features resulting from
pretreatment, thus the hydrolysis and pretreatment processes are intimately coupled
because of structural features.
Model lignocelluloses with various structural features were hydrolyzed with a
variety of cellulase loadings for 1, 6, and 72 h. Glucan, xylan, and total sugar
conversions at 1, 6, and 72 h were linearly proportional to the logarithm of cellulase
loadings from approximately 10% to 90% conversion, indicating that the simplified
HCH-1 model is valid for predicting lignocellulose digestibility. Carbohydrate
conversions at a given time versus the natural logarithm of cellulase loadings were
plotted to obtain the slopes and intercepts which were correlated to structural features (lignin content, acetyl content, cellulose crystallinity, and carbohydrate content) by both
parametric and nonparametric regression models.
The predictive ability of the models was evaluated by a variety of biomass (corn
stover, bagasse, and rice straw) treated with lime, dilute acid, ammonia fiber explosion
(AFEX), and aqueous ammonia. The measured slopes, intercepts, and carbohydrate
conversions at 1, 6, and 72 h were compared to the values predicted by the parametric
and nonparametric models. The smaller mean square error (MSE) in the parametric
models indicates more satisfactorily predictive ability than the nonparametric models.
The agreement between the measured and predicted values shows that lignin content,
acetyl content, and cellulose crystallinity are key factors that determine biomass
digestibility, and that biomass digestibility can be predicted over a wide range of
cellulase loadings using the simplified HCH-1 model.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4314 |
| Date | 30 October 2006 |
| Creators | Zhu, Li |
| Contributors | Holtzapple, Mark T. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 1500948 bytes, electronic, application/pdf, born digital |
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