The week's pick
Random Articles
Reseach Article
Comparative Study between Neural Network Model and Mathematical Models for Prediction of Glucose Concentration during Enzymatic Hydrolysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 56 - Number 1 |
| Year of Publication: 2012 |
| Authors: M. Nikzad, K. Movagharnejad, F. Talebnia |
10.5120/8859-2818
|
M. Nikzad, K. Movagharnejad, F. Talebnia . Comparative Study between Neural Network Model and Mathematical Models for Prediction of Glucose Concentration during Enzymatic Hydrolysis. International Journal of Computer Applications. 56, 1 ( October 2012), 43-48. DOI=10.5120/8859-2818
Abstract
Enzymatic hydrolysis of cellulose is a complex process because of a number of inhibition and enzyme inactivation reactions which happen during hydrolysis. Artificial Neural Networks (ANNs) are very effective in developing predictive models for processes involving complex reaction kinetics that would otherwise be difficult to be modeled by more traditional deterministic approaches. The present investigation was carried out to study the application of Artificial Neural Network as a tool for predicting glucose production by enzymatic hydrolysis of pure cellulose and comparison with mathematical models and experimental results. A feed forward neural network with one hidden layer was trained and used to predict the glucose production. Comparing the R2 (coefficient of determination), MSE (mean square error) and ARD (average relative deviation) values of the neural network model with the mathematical model, it was concluded that the neural network is more accurate than the mathematical models. The obtained results show that the ANN can be a useful method for the design of the enzymatic hydrolysis.
References
- L. R. Lynd, J. H. Cushman, R. J. Nichols, C. E. Wyman, "Fuel ethanol from cellulosic biomass", Science, 251 (1991) 1318-1323.
- Y. Sun J. Cheng, "Hydrolysis of lignocellulosic materials for ethanol production: a review", Bioresource technology, 83 (2002) 1-11.
- F. Corazza, L. Calsavara, F. Moraes, G. Zanin, I. Neitzel, "Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling", Brazilian Journal of Chemical Engineering, 22 (2005) 19-29.
- C. H. Stephens, P. M. Whitmore, H. R. Morris, M. E. Bier, "Hydrolysis of the amorphous cellulose in cotton-based paper", Biomacromolecules, 9 (2008) 1093-1099.
- S. A. Kalogirou, S. Panteliou, A. Dentsoras, "Artificial neural networks used for the performance prediction of a thermosiphon solar water heater", Renewable energy, 18 (1999) 87-99.
- J. A. Silva, E. H. Costa Neto, W. S. Adriano, A. L. O. Ferreira, L. R. B. Gonçalves, "Use of neural networks in the mathematical modelling of the enzymic synthesis of amoxicillin catalysed by penicillin G acylase immobilized in chitosan", World Journal of Microbiology and Biotechnology, 24 (2008) 1761-1767.
- A. H. Geeraerd, C. H. Herremans, L. R. Ludikhuyze, M. E. Hendrickx, J. F. Van Impe, "Modeling the kinetics of isobaric-isothermal inactivation of Bacillus subtilis ?-amylase with artificial neural networks", Journal of food engineering, 36 (1998) 263-279.
- V. Saucedo, B. Eikens, M. Karim, "Identification techniques for a recombinant fed-batch fermentation for ethanol production", Advances in Bioprocess Engineering, 275 (1994).
- T. J. McAvoy, H. T. Su, N. S. Wang, M. He, J. Horvath, H. Semerjian, "A comparison of neural networks and partial least squares for deconvoluting fluorescence spectra", Biotechnology and bioengineering, 40 (1992) 53-62.
- S. Lertworasirikul, "Drying kinetics of semi-finished cassava crackers: A comparative study", LWT-Food Science and Technology, 41 (2008) 1360-1371.
- B. Nidetzky W. Steiner, "A new approach for modeling cellulase–cellulose adsorption and the kinetics of the cellulose in heterogeneous solid-liquid systems", Biochemical engineering journal, 4 (2000) 197-206.
- K. Movagarnejad, M. Sohrabi, T. Kaghazchi, F. Vahabzadeh, "A model for the rate of enzymatic hydrolysis of enzymatic hydrolysis of microcrystalline cellulose", Biotechnology and bioengineering, 42 (1993) 469-479.
- M. Kashaninejad, A. Dehghani, M. Kashiri, "Modeling of wheat soaking using two artificial neural networks (MLP and RBF)", Journal of food engineering, 91 (2009) 602-607.
- I. Bardot, N. Martin, G. Trystram, L. Bochereau, M. Rogeaux, J. Hossenlopp, "A New Approach for the Formulation of Beverages, II: Interactive Automatic Method", LWT-Food Science and Technology, 27 (1994) 513-521.
- M. Dornier, M. Decloux, G. Trystram, A. Lebert, "Dynamic modeling of crossflow microfiltration using neural networks", Journal of membrane science, 98 (1995) 263-273.
- P. Teissier, B. Perret, E. Latrille, J. Barillere, G. Corrieu, "A hybrid recurrent neural network model for yeast production monitoring and control in a wine base medium", Journal of biotechnology, 55 (1997) 157-169.
- B. Mehdizadeh K. Movagharnejad, "A comparison between neural network method and semi empirical equations to predict the solubility of different compounds in supercritical carbon dioxide", Fluid Phase Equilibria, 303 (2011) 40-44.
- K. Movagharnejad, B. Mehdizadeh, M. Banihashemi, M. S. Kordkheili, "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network", Energy(Oxford), 36 (2011) 3979-3984.
- A. Singh, D. Tatewar, P. Shastri, S. Pandharipande, "Application of ANN for prediction of cellulase and xylanase production by Trichoderma reesei under SSF condition", Indian Journal of Chemical Technology, 15 (2008) 53-58.
- D. E. Rumelhart, G. E. Hintont, R. J. Williams, "Learning representations by back-propagating errors", Nature, 323 (1986) 533-536.
- A. Bucinski, M. Karamaç, R. Amarowicz, "Application of artificial neural networks for modelling pea protein hydrolysis by trypsin", 13 (2004) 163-168.
Index Terms
Keywords