Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks

The aim of this work was to model and predict the process of bioethanol production from intermediates and byproduct of sugar beet processing by applying artificial neural networks. Prediction of one substrate fermentation by neural networks had the same input variables (fermentation time and starting sugar content) and one output value (ethanol content, yeast cell number or sugar content). Results showed that a good prediction model could be obtained by networks with single hidden layer. The neural network configuration that gave the best prediction for raw or thin juice fermentation was one with 8 neurons in hidden layer for all observed outputs. On the other side, the optimal number of neurons in hidden layer was found to be 9 and 10 for thick juice and molasses, respectively. Further, all substrates data were merged, which led to introducing an additional input (substrate type) and defining all outputs optimal network architecture to 3-12-1. From the results the conclusion was that artificial neural networks are a good prediction tool for the selected network outputs. Also, these predictive capabilities allowed the application of the Garson's equation for estimating the contribution of selected process parameters on the defined outputs with satisfactory accuracy.

[1]  Jovana Grahovac,et al.  Optimization of ethanol production from thick juice: A response surface methodology approach , 2012 .

[2]  Zoltan K. Nagy,et al.  Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks , 2007 .

[3]  Jenny Ní Mhurchú,et al.  Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks , 2006 .

[4]  Vincenza Calabrò,et al.  A hybrid neural approach to model batch fermentation of "ricotta cheese whey" to ethanol , 2010, Comput. Chem. Eng..

[5]  Jovana Grahovac,et al.  INTERPRETING THE NEURAL NETWORKFOR PREDICTION OF FERMENTATION OF THICK JUICE FROM SUGAR BEET PROCESSING , 2011 .

[6]  G A Hill,et al.  Effects of high product and substrate inhibitions on the kinetics and biomass and product yields during ethanol batch fermentation , 1992, Biotechnology and bioengineering.

[7]  Shankararaman Chellam,et al.  Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions , 2005 .

[8]  Eric Latrille,et al.  Application of artificial neural networks for crossflow microfiltration modelling: “black-box” and semi-physical approaches , 1997 .

[9]  M. Ergun,et al.  Application of a statistical technique to the production of ethanol from sugar beet molasses by Saccharomyces cerevisiae , 2000 .

[10]  Jovana Grahovac,et al.  Optimization of bioethanol production from intermediates of sugar beet processing by response surface methodology. , 2011 .

[11]  Aleksandar Jokić,et al.  Bioethanol Production from Raw Juice as Intermediate of Sugar Beet Processing: A Response Surface Methodology Approach , 2010 .

[12]  Jovana Grahovac,et al.  Future trends of bioethanol co-production in Serbian sugar plants , 2012 .

[13]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[14]  Mohamed Meselhy Eltoukhy,et al.  The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. , 2010, Journal of hazardous materials.

[15]  M. Moo-young,et al.  Ethanol fermentation technologies from sugar and starch feedstocks. , 2008, Biotechnology advances.

[16]  E. B. Gueguim Kana,et al.  Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm. , 2012 .

[17]  Zoltan Z. Zavargo,et al.  Artificial neural network approach to modelling of alcoholic fermentation of thick juice from sugar beet processing , 2012 .