Science and Education Publishing American Journal of Modeling and Optimization 1 1 2013 07 01 Prediction of Ethanol Concentration in Biofuel Production Using Artificial Neural Networks 31 35 EN H. Ahmadian-Moghadam Institute of Biological science, College of Art and Science, University of Philippines Los Ba?os, Laguna, Philippines F. B. Elegado R. Nayve AJMO2013132 10.12691/ajmo-1-3-2 2013 05 14 2013 06 30 2013 07 01 Environmentally friendly and important enhancements in biofuel production technology are necessary in order to cut back production prices and create it as a competitive resource material. This study performed a cost-effective bioprocess to provide bio-ethanol from sugarcane molasses by chosen strains of the yeast S. cerevisiae, through experiments at laboratory, pilot and industrial scales. Arti?cial neural networks are shown to be powerful tools for system modeling. The objective of this study was to develop a straightforward, accurate and time saving prognosticative model for alcohol production. Results recommend that artificial neural networks provide a good means of effective recognizing patterns in data and accurately predicting ethanol concentration based on investigating inputs. The ethanol concentration evaluated in experiments of industrial biofuel production and this research develops a simple, accurate, nondestructive and time saving artificial neural networks model for estimation of ethanol concentration in batch ethanol fermentation from molasses based on live and dead yeast cells, sugar concentration.