The effect of selected compositional features on enzymatic hydrolysis

Lignocellulosic biomass is one of the most valuable alternative energy sources because it is renewable, widely available, and environmentally friendly. Biomass could be converted to liquid fuels (such as ethanol) or chemicals (such as carboxylic acids). This technology has many potential benefits e.g., reduced dependence on oil and reduced greenhouse gas emissions. A simplified form of a theoretical model of cellulose hydrolysis, the HCH-1 Model, was used to model enzymatic hydrolysis. The neural network toolbox in MATLAB and SAS were used to develop nonparametric and parametric empirical models, respectively, to predict enzymatic digestibility. The models were tested to determine if they could predict enzymatic reactivity of biomass samples pretreated via aqueous ammonia, FIBEX, lime, neutral water, and dilute acid. The analyses were compared with outputs from the mathematical models to determine if they could predict enzymatic digestibility based solely on acetyl content, lignin content, and crystallinity or determine if there were other structural features that play a major role in the enzymatic hydrolysis of biomass. This is an abstract of a paper presented at the AIChE Annual Meeting and Fall Showcase (Cincinnati, OH 10/30/2005-11/4/2005).

The effect of selected compositional features on enzymatic hydrolysis Conference Paper