Artificial Neural Network Model Prediction of Glucose by Enzymatic Hydrolysis of Rice Straw - Jaya, Erniza Mohd Johan; Norhalim, Nur Atiqah; Ahmad, Zainal (Taylor's University, 2014)
The aim of this paper is to predict the production of glucose using artificial neural network (ANN) and validation with the experimental values for hydrolysis process. The ANN consists of three layers which are input, hidden and output layer. The input layer is the manipulated variables in the case study, which are the activity of added cellulose, substrate initial concentration and hydrolysis time on the production of glucose while the output layer is the concentration of glucose. The performances of the model were evaluated using the coefficient of determination, mean square error and average relative deviation. The predictive model shows a good result as the coefficient of determination, 0.8361 was obtained with a small value of mean square error, 0.1947 and 5.644 as the average relative deviation. It clearly shows that ANN gives a good prediction on the enzymatic hydrolysis for the production of glucose