Use of Artificial Neural Networks for Prediction of Cell Mass and Ethanol Concentration in Batch Fermentation using Saccharomyces cerevisiae Yeast

Abstract

The application of neural network (ANN) for the prediction of fermentation variables in batch fermenter for the production of ethanol from grape waste using Saccharomyces cerevisiae yeast has been discussed in this article. Artificial neural network model, based on feed forward architecture and back propagation as training algorithm, is applied in this study. The Levenberg- Marquardt optimization technique has been used to upgrade the network by minimizing the sum square error (SSE). The performance of the network for predicting cell mass and ethanol concentration is found to be very effective. The best prediction is obtained using a neural network with two hidden layers consisting of 15 and 16 neurons, respectively.