Submitted to: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)
Publication Type: Proceedings
Publication Acceptance Date: 7/1/2006
Publication Date: 7/9/2006
Citation: Murthy, G.S., Singh, V., Rausch, K., Johnston, D., Tumbleson, M.E. Mathematical modeling of enzymatic hydrolysis of starch: application to fuel ethanol production. 2006. Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)Paper #066229. Interpretive Summary:
Technical Abstract: Enzymatic hydrolysis of starch in corn is an important step that determines fermentation efficiency. Corn genetics, post harvest handling and process conditions are factors that affect starch hydrolysis. There is a lack of mathematical models for starch hydrolysis in the dry grind corn process that can predict mono, di and trisaccharide production and dextrose equivalent (DE) of mash. A model was developed based on the cluster/tree structure of amylopectin and a Monte Carlo technique to predict DE and sugar (mono, di and trisaccharide) concentrations under varying process conditions. The model considers effects of corn starch content, amylose and amylopectin ratio, mash solids content, enzyme dosage, temperature and pH during liquefaction and saccharification. Validation was performed by measuring sugar production profiles using an HPLC method. The standard deviations of the model predictions for glucose concentration and DE values were less than plus/minus 0.15 (%w/v) and plus/minus 0.35 (%), respectively. Experiment profiles followed the simulated profiles for glucose and maltose for high amylose and waxy corn starches. However, the experimental values for higher sugars and dextrins had greater deviations from the simulated values which could be attributed to measurement errors for higher sugars and dextrins with the HPLC method. Waxy corn starch showed a closer agreement with model predictions for liquefaction and saccharification steps as compared to high amylose corn starch. This model can be used to study effects of process conditions and corn genetics on liquefaction and saccharification.