Simulation and Optimization of Biosorption Studies for Prediction of Sorption Efficiency of Leucaena Leucocephala Seeds for the Removal of Ni (II) From Waste Water

  • J. K. Arora
  • S. Srivastava

Abstract

Simulation and optimization of biosorption studies were carried out using Artificial Neural Network (ANN) modeling. A single layer ANN model was developed to simulate the process and to predict the removal efficiency of Ni (II) ions from aqueous solution using Leucaena Leucocephala seed powder (LLSP). Different NN architecture was tested by varying network topology. The findings indicated that the ANN provided reasonable predictive performance. The influence of each parameter on the variable studied was assessed, and metal concentration, contact time, biomass dosage and initial volume were found to be the most significant factors. Simulations based on the developed ANN model can estimate the behavior of the biosorption phenomenon process under different conditions.

Keywords

Artificial Neural Network Mean Square Error Minimum Mean Square Error Back Propagation Algorithm Back Propagation Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    R. A. K. Rao, M.A. Khan, Colloids and Surfaces A: Physicochem. Eng. Aspects 332 (2009) 121–128.CrossRefGoogle Scholar
  2. 2.
    M.N. Nourbakhsh, S. Kilicarshan, S. Ilhan, H. Ozdag, Chem. Eng. J. 85 (2002) 351–355.CrossRefGoogle Scholar
  3. 3.
    T.A. Kurniawan, G.Y.S. Chan, W.H. Lo, S. Babel, Chem. Eng. J. 118 (2006) 83–98.CrossRefGoogle Scholar
  4. 4.
    H.K. Alluri, S.R. Ronda, V.S. Settalluri, J.S. Bondili, V. Suryanarayana, P. Venkateshwar, African J. Biotech. 6 (2007) 2924–2931.Google Scholar
  5. 5.
    J. Febrianto, A.N Kosasih, J. Sunarso, Y.H Jua, N. Indraswati, S. Ismadji, J. Haz. Mat. 162 (2009) 616–645.CrossRefGoogle Scholar
  6. 6.
    Y.S. Park, T.S. Chon, I.S. Kwak, S. Lek, Science of the Total Environ, 327, (2004) 105–122.CrossRefGoogle Scholar
  7. 7.
    L. Belanche, J.J. Valdes, J. Comas, I.R. Roda, M. Poch, Artif. Intell. Eng, 14, (2000) 307–317.CrossRefGoogle Scholar
  8. 8.
    G.R. Shetty, S. Chellam, J. Membrane Sci, 217 (2003) 69–86.CrossRefGoogle Scholar
  9. 9.
    A. Kardam, K.R. Raj, J.K. Arora, S. Srivastava, Natl. Acad. Sci. Lett. 33 (3&4) (2010) 83–87.Google Scholar
  10. 10.
    K.R. Raj, A. Kardam, J.K. Arora, S. Srivastava, J. Radioanal. Nucl. Chem. 283 (2010) 797–801.CrossRefGoogle Scholar
  11. 11.
    A. Kardam, K.R. Raj, J.K. Arora, S. Srivastava, J. Water Resource and Protection 2 (2010) 339–344. CrossRefGoogle Scholar
  12. 12.
    K.R. Raj, A. Kardam, J.K. Arora, S. Srivastava, J. Water Resource and Protection 2 (2010) 331–338.CrossRefGoogle Scholar
  13. 13.
    A. Kardam, P. Goyal, J.K. Arora, K.R. Raj, S. Srivastava, Natl. Acad. Sci. Lett. 32 (2009) 179–181.Google Scholar
  14. 14.
    P. Goyal, P. Sharma, S. Srivastava, M.M. Srivastava, Int. J. Environ. Sci. Technol. 5 (2008) 27–34.Google Scholar
  15. 15.
    P. Goyal, S. Srivastava, Arch. Environ. Prot. 34 (2008) 35–45.Google Scholar
  16. 16.
    P. Goyal, S. Srivastava, Natl. Acad. Sci. Lett. 31 (2008) 347–351.Google Scholar
  17. 17.
    P. Goyal, S. Srivastava, Journal of Hazardous Materials, 172 (2009) 1206–1211.CrossRefGoogle Scholar
  18. 18.
    D.R. Baughman, Y.A. Lieu. Neural network in bioprocessing and chemical Engineering, Academic Press. San Diego, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • J. K. Arora
    • 1
  • S. Srivastava
    • 2
  1. 1.Department of MathematicsTechnical CollegeAgraIndia
  2. 2.Department of Chemistry, Faculty of ScienceDayalbagh Educational InstituteAgraIndia

Personalised recommendations