American Journal of Modeling and Optimization
ISSN (Print): 2333-1143 ISSN (Online): 2333-1267 Website: http://www.sciepub.com/journal/ajmo Editor-in-chief: Dr Anil Kumar Gupta
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American Journal of Modeling and Optimization. 2013, 1(3), 31-35
DOI: 10.12691/ajmo-1-3-2
Open AccessArticle

Prediction of Ethanol Concentration in Biofuel Production Using Artificial Neural Networks

H. Ahmadian-Moghadam1, , F. B. Elegado2 and R. Nayve2

1Institute of Biological science, College of Art and Science, University of Philippines Los Baños, Laguna, Philippines

2National Institutes of Molecular Biology and Biotechnology, University of Philippines Los Baños, Laguna, Philippines

Pub. Date: July 01, 2013

Cite this paper:
H. Ahmadian-Moghadam, F. B. Elegado and R. Nayve. Prediction of Ethanol Concentration in Biofuel Production Using Artificial Neural Networks. American Journal of Modeling and Optimization. 2013; 1(3):31-35. doi: 10.12691/ajmo-1-3-2

Abstract

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.

Keywords:
artificial neural networks yeast alcohol production prediction

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References:

[1]  Ahmadian-Moghadam, H. 2012. Prediction of pepper (Capsicum annuum) leaf area using Group Method of Data Handling-Type Neural Networks. International Journal of AgriScience Vol. 2(11):993-999.
 
[2]  Ahmadi H., A.Golian, M.Mottaghitalab, and N.Nariman-Zadeh. Prediction model for true metabolizable energy of feather meal and poultry offal meal using group method of data handling-type neural network. Poultry Science. 87(9), 1909-1912. 9-1-2008.
 
[3]  Ahmadi H., M.Mottaghitalab, and N.Nariman-Zadeh. Group Method of Data Handling-Type Neural Network Prediction of Broiler Performance Based on Dietary Metabolizable Energy, Methionine, and Lysine. The Journal of Applied Poultry Research 16(4), 494-501. 12-21-2007.
 
[4]  Bagheri, A., N. Nariman-Zadeh, A. Jamali, K. Dayjoori. 2009. Design of ANFIS Networks Using Hybrid Genetic and SVD Method for the Prediction of Coastal Wave Impacts. Applications of Soft Computing Advances in Intelligent and Soft Computing Volume 58, 2009, pp 83-92.
 
[5]  Behera, S., S. Kar, R.C. Mohanty and R.C. Ray. (2010) Comparative study of bioethanol production from mahula (Madhucalatifolia L.) flowersby Saccharomyces cerevisiae cells immobilized in agar agar and Ca-alginate matrices. Appl Energy. 87:96-100.
 
[6]  Borjesson, P. (2009) Good or bad bioethanol from a greenhouse gas perspective-what determine this? ApplEnergy. 86:589-94.
 
[7]  Cardona-Alzate, C.A., Sanchez-Toro, O.J. (2006) Energy consumption analysis of integrated flowsheets for production of fuel ethanol from lignocellulosic biomass. Energy 31, 2447-2459.
 
[8]  Dubois, M., K.A. Gilles, J.K. Hamilton, P.A. Rebers and F. Smith. 1956. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28:350-356.
 
[9]  Holzberg, I., Finn, R.K., Steinkraus, K.H. (1967) Kinetic study of the alcoholic fermentation of grape juice.Biotechnol. Bioeng. 9, 413-423.
 
[10]  Gnansounou, E., Dauriat, A., Wyman, C.E. (2005) Refining sweet sorghum to ethanol and sugar: economic trade-offs in the context of North China. Bioresour.Technol. 96, 985-1002.
 
[11]  Ivakhnenko, A. G. (1971) Polynomial theory of complex systems. IEEE Trans., Syst. Man Cybern. MC-1. 4:364-378.
 
[12]  Kiani Deh Kiani, M., B. Ghobadian, T. Tavakoli, A.M. Nikbakht and G. Najafi. (2010) Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends. Energy 35 (2010) 65-69.
 
[13]  Kopsahelis, N., Agouridis, N., Bekatorou, A., Kanellaki, M. (2007) Comparative study of spent grains and delignified spent grains as yeast supports for alcohol production from molasses. Bioresour. Technol. 98, 1440-1447.
 
[14]  Nariman-Zadeh, N., A. Darvizeh, A. Jamali, and A. Moieni (2005) Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process. J. Mater. Process. Technol. 164-165:1561-1571.
 
[15]  NESEA. (2002) Factsheet: Ethanol. Available at: http://www.nesea.org/greencarclub/factsheets_ethanol.pdf. Accessed on 23 January 2008.
 
[16]  Oberstone, J. (1990) Management science concepts, insights, and applications. west wubl. co., new york, NY.
 
[17]  Wyman, C.E., (1994) Ethanol from lignocellulosic biomass: technology, economics and opportunities. Bioresour. Technol. 50, 3-16.
 
[18]  Xu, T.J., Zhao, X.Q., Bai, F.W. (2005) Continuous ethanol production using selfflocculating yeast in a cascade of fermentors. Enzyme Microbiol. Technol. 37, 634-640.
 
[19]  Yuji, O., Nakamura K (2009) Production of ethanol from the mixture of beet molasses and cheese whey by a 2-deoxylucose-resistant mutant of Klyveromyces marxianus. FEMSYeastRes. 9:472-8.