Issue

Vol. 25 No. 4 (2013): Vol 25 Issue 4

Copyright (c) 2013 AJC

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Neural Network for Modeling of Chemical Reaction Systems: Kinetics of Concentrated Acid Hydrolysis of Walnut Green Skin

https://doi.org/10.14233/ajchem.2013.13119
A. Arasteh Nodeh
Department of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
M. Ardjmand
Department of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
M.A. Fanaei
Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
A.A. Safekordi
Department of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Corresponding Author(s) : A. Arasteh Nodeh

aliarastehnodeh@yahoo.com

Asian Journal of Chemistry, Vol. 25 No. 4 (2013): Vol 25 Issue 4

Abstract

Besides usage of walnut green skin as an available biomass for bioethanol production, application of neural network for modeling the conversion rates of glucose from walnut green skin by concentrated acid hydrolysis has been investigated. Due to a more complex and unknown kinetics of the investigated reaction, the proposed approach based on application of neural networks is an efficient and accurate tool to solve modeling problem. The influence of the main pretreatment variables including temperature, processing time, solid contents and acid concentration on fermentable sugar generation from walnut green skin is studied by using factorial design of experiment. Levels for pretreatment temperature (65, 80, 90 ºC), process time (120, 180, 240 min), solid content (5, 10, 15 %) and concentration of sulfuric acid (20, 40, 60 %) were selected. Glucose were analyzed by HPLC and modeled by two layers neural network with different topology. Despite using simple data and a simple neural network, high accuracy was achieved. Results was compared with a quadratic equation model.

Keywords

Walnut green skin Glucose Acid sulfuric Concentrated acid hydrolysis Neural network
Arasteh Nodeh, A., Ardjmand, M., Fanaei, M., & Safekordi, A. (2012). Neural Network for Modeling of Chemical Reaction Systems: Kinetics of Concentrated Acid Hydrolysis of Walnut Green Skin. Asian Journal of Chemistry, 25(4), 1793–1799. https://doi.org/10.14233/ajchem.2013.13119
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. R.K. Sukumaran, R.R. Singhania, G.M. Mathew and A. Pandey, Renewable Energy, 34, 421 (2009).
  2. I. Romero, E. Ruiz, E. Castro and M. Maya, Chem. Eng. Res. Des., 88, 633 (2010).
  3. A. Demirbas, Energy Sour., 27, 327 (2005).
  4. J.W. Kim and G. Mazza, Ind. Crops Prod., 28, 346 (2008).
  5. K.A. Gray, L. Zhao and M. Emptage, Curr. Opin. Chem. Biol., 10, 141 (2006).
  6. G. Shama, Proc. Biochem., 23, 138 (1988).
  7. E. Sherrard and F. Kressman, Ind. Eng. Chem., 37, 5 (1945).
  8. A.K. Chandel, E.S. Chan, R. Rudravaram, M.L. Narasu, L. Venkateswar and P. Ravindra, Biotechnol. Mol. Biol. Rev., 2, 14 (2007).
  9. A. Hashem and M. Rashad, Egypt J. Food Sci., 21, 171 (1993).
  10. M.T. Taherzadeand and K. Karimi, Bioresources, 2, 472 (2007).
  11. M. Balat, H. Balat and C. Oz, Prog. Ener. Combust. Sci., 34, 551 (2008).
  12. P. Lenihan, A. Orozco, E. O'nill, M.N.M. Ahmad, D.W. Rooney and G.M. Walker, Chem. Eng. J., 156, 395 (2010).
  13. E. Plamqvist, J.S. Almeida and B. Hahn, Biotechnol. Bioengg., 62, 447 (1997).
  14. E. Palmqvist and B. Hahn-Hägerdal, Bioresour. Technol., 74, 25 (2000).
  15. R. Aguilar, J.A. Ramirez, G. Garrote and M. Vazquez, J. Food. Eng., 55, 309 (2002).
  16. T. Rogalinski, T. Ingram and G. Brunner, J. Supercrit. Fluids, 47, 54 (2008).
  17. E. Castro, M. Diaz, C. Cara, E. Ruiz and M. Romero I.M. Moya, Bioresour. Technol., 102, 1270 (2011).
  18. T. Jeong, B. Um, J. Kim and K. Oh, Appl. Biochem. Biotechnol., 161, 22 (2010).
  19. J. Zhou, Y.H. Wang, J. Chu, L.Z. Luo, Y.P. Zhuang and S.L. Zhang, Bioresour. Technol., 100, 819 (2009).
  20. G. Luo, F. Talennia, D. Krakahev, L. Xie, Q. Zhou and I. Angelidaki, Bioresour. Technol., 102, 1433 (2011).
  21. M.A. Bezerra, R.E. Santelli, E.P. Oliviera, L.S. Villar and L.A. Escaleira, Talanta, 76, 965 (2008).
  22. J.F. Saeman, Ind. Eng. Chem., 37, 43 (1945).
  23. T. Rogalinski, T. Ingram and G. Brunner, J. Supercrit. Fluids, 47, 54 (2008).
  24. L.A. Malester, M. Green, S. Kimchie and G. Shelef, Biol. Wastes, 26, 115 (1988).
  25. L.A. Malester, M. Green and G. Shelef, Ind. Eng. Chem. Res., 31, 1998 (1992).
  26. A. Orozco, M. Ahmad, D. Rooney and G.M. Walker, Proc. Safety Environ. Protect. Trans. IChemE, Part B, 85, 446 (2007).
  27. Y. Zhang, X. Jing-Liang and Z.H. Yuan, Modeling and Prediction in the Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks, Fifth International Conference on Natural Computation, 28 December, pp. 158-162 (2009).
  28. E. Rivera, S. Rabelo, D. Garcia, R. Filho and A. Costa, J. Chem. Technol. Biotechnol., 85, 983 (2010).
  29. R. Rashid, H. Jamaluddin and N. Amin, Appl. Artific. Intellig., 20, 1 (2006).
  30. Y. Daizo, K. Masaaki, S. Satoshi, N. Kiyotaka and K. Hideki, J. Phys. Chem., 113, 3181 (2009).
  31. M.T. Vafaei, R. Eslamloueyan and Sh. Ayatollahi, Chem. Eng. Res. Design, 87, 997 (2009).
  32. E.J. Molga, B.A.A. van Woezik and K.R. Westerterp, Chem. Eng. Process., 39, 323 (2000).
  33. A. Abilov and Z. Zeybek, Chem. Eng. Process., 39, 449 (2000).
  34. D. Aguado, J. Ribes, T. Montoya, J. Ferrera and A. Secob, Comp. Chem. Eng., 33, 465 (2009).
  35. M.R. Rowell and A. Young, Paper and Composite from Agrobased Resources, CRC Press, p. 46 (1997).
  36. L.E. Wise, M. Murphy and D'Adieco, Pap. Trade J., 122, 35 (1946).
Read More
Author biographies is not available.
Download
PDF
Statistic
Read Counter : 1 Download : 0