Prediction of Ethanol Concentration in Biofuel Production Using Artificial Neural Networks

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. Artificial 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.

[1]  Cardona Alzate,et al.  Energy consumption analysis of integrated flowsheets for production of fuel ethanol from lignocellulosic biomass , 2006 .

[2]  Charles E. Wyman,et al.  Ethanol from lignocellulosic biomass: Technology, economics, and opportunities , 1994 .

[3]  Ali Moeini,et al.  Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process , 2005 .

[4]  Nader Nariman-zadeh,et al.  Design of ANFIS Networks Using Hybrid Genetic and SVD Method for the Prediction of Coastal Wave Impacts , 2009 .

[5]  R. K. Finn,et al.  A kinetic study of the alcoholic fermentation of grape juice , 1967 .

[6]  Nikolaos Kopsahelis,et al.  Comparative study of spent grains and delignified spent grains as yeast supports for alcohol production from molasses. , 2007, Bioresource technology.

[7]  A. G. Ivakhnenko,et al.  Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..

[8]  Ramesh C. Ray,et al.  Comparative study of bio-ethanol production from mahula (Madhuca latifolia L.) flowers by Saccharomyces cerevisiae cells immobilized in agar agar and Ca-alginate matrices , 2010 .

[9]  Pål Börjesson,et al.  Good or bad bioethanol from a greenhouse gas perspective – What determines this? , 2009 .

[10]  H. Ahmadian-Moghadam,et al.  Prediction of pepper (Capsicum annuum L.) leaf area using group method of data handling-type neural networks , 2012 .

[11]  E Gnansounou,et al.  Refining sweet sorghum to ethanol and sugar: economic trade-offs in the context of North China. , 2005, Bioresource technology.

[12]  Hamed Ahmadi,et al.  Group Method of Data Handling-Type Neural Network Prediction of Broiler Performance Based on Dietary Metabolizable Energy, Methionine, and Lysine , 2007 .

[13]  Y. Oda,et al.  Production of ethanol from the mixture of beet molasses and cheese whey by a 2-deoxyglucose-resistant mutant of Kluyveromyces marxianus. , 2009, FEMS yeast research.

[14]  N. Nariman-zadeh,et al.  Prediction model for true metabolizable energy of feather meal and poultry offal meal using group method of data handling-type neural network. , 2008, Poultry science.

[15]  Xin-Qing Zhao,et al.  Continuous ethanol production using self-flocculating yeast in a cascade of fermentors , 2005 .

[16]  Gholamhassan Najafi,et al.  Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends , 2010 .

[17]  F. Smith,et al.  COLORIMETRIC METHOD FOR DETER-MINATION OF SUGAR AND RELATED SUBSTANCE , 1956 .