Ammonia Based Pretreatment Optimization of Cornstover Biomass Using Response Surface Methodology and Artificial Neural Network

Abstract

Effective pretreatment of lignocellulosic biomass could be used to produce fermentable sugar for renewable energy production, which reduces problems related to nonrenewable fuel. Therefore, the purpose of this study was to produce monosaccharide sugar for renewable energy from agricultural waste via ammonia pretreatment optimization using response surface methodology (RSM) and artificial neural network (ANN). Cornstover was collected and mechanically pre-treated. RSM and ANNwere applied for experimental design and optimum parameters estimation. Cornstover was converted into simple sugars with a combination of ammonia treatment subsequently enzymatic hydrolysis. The maximum yield of glucose (87.46%), xylose (77.5%), and total sugar (442.0g/Kg) were all accomplished at 20 min of residence time, 4.0 g/g of ammonia loading, 132.5 0C of temperature, and 0.5 g/g of water loading experimentally. While 86.998% of glucose, 76.789% of xylose, and 439.323(g/Kg) of total sugar were achieved by prediction of the ANN model. It was shown that cornstover has a massive potential sugar for the production of renewable fuel. Ammonia loading had a highly significant effect on the yield of all sugars compared to other parameters. Interactively, ammonia loading and residence time had a significant impact on the yield of glucose, while water loading and residence time, had a significant effect on the yield of xylose. The accuracy and prediction of an artificial neural network are better than that of the response surface methodology.