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Modeling and differential evolution optimization of PAN carbon fiber production process

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Abstract

In this research, results of an experimental and artificial neural network fuzzy interface system (ANFIS) modeling of operating parameters on tensile strength of the carbon fibers are investigated. To do these experiments, the commercial polyacrylonitrile (PAN) fiber of Polyacryl Iran Corporation (PIC) was used as the precursors. The results show that increasing all of parameters improves tensile strength performance. ANFIS was applied to predict tensile strength of carbon fibers as a function of stabilization temperature at first stage (STFIS), stabilization temperature at second stage (STSS), stabilization temperature at third stage (STTS), stabilization temperature at fourth stage (STFOS), and carbonization temperature (CT). The optimum levels of influential factors, determined for tensile strength are STFIS 200 °C, STSS 225 °C, STTS 240 °C, STFOS 260 °C, CT, and 1400 °C. The modeling results showed that there is an excellent agreement between the experimental data and the predicted values. Furthermore, the fiber process is optimized applying differential evolution (DE) algorithm as an effective and robust optimization method.

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References

  1. Z. Wangxi, L. Jie, and W. Gang, Carbon, 41, 2805 (2003).

    Article  CAS  Google Scholar 

  2. H. Ahmadi Danesh Ashtiani, and R. Eslami Farsani, Fiber Polym., 12, 1054 (2011).

    Google Scholar 

  3. Z. Wangxi, L. Jie, and W. Gang, Carbon, 41, 2805 (2003).

    Article  CAS  Google Scholar 

  4. M. A. Montes-Mor’an, W. Gauthier, and A. Mart’inez-Alonso, Carbon, 42, 1275 (2004).

    Article  CAS  Google Scholar 

  5. M. Trinquecoste, J. L. Carlier, A. Derrb, and P. Delhaes, Carbon, 34, 923 (1996).

    Article  CAS  Google Scholar 

  6. A. Sedghi, R. EslamiFarsani, and A. Shokuhfar, J. Mater. Process. Technol., 198, 60 (2008).

    Article  CAS  Google Scholar 

  7. M. S. A. Rahaman, A. F. Ismail, and A. Mustafa, Polym. Degrad. Stab., 92, 1421 (2007).

    Article  CAS  Google Scholar 

  8. F. S. Mjalli, S. Al-Asheh, and H. E. Alfadala, J. Environ. Manage., 83, 329 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. J. S. R. Jang and C. T. Sun, Proc. IEEE, 83, 378 (1995).

    Article  Google Scholar 

  10. R. J. S. Jang, and N. Gulley, “Fuzzy Logic Toolbox”, The Mathworks Inc., Natick, MA, 1995.

    Google Scholar 

  11. J. S. R. Jang, Proc. of the Fifth IEEE Int. Conf. on Fuzzy Systems, USA, 3, 1897 (1996).

    Google Scholar 

  12. K. Price and R. Storn, Dr. Dobb’s Journal of Computer Calisthenics & Orthodontia, 18 (1997).

    Google Scholar 

  13. O. P. Bahl, L. M. Manocha, G. C. Jain, S. S. Chari, and G. Bhatia, J. Scient. Indus. Res., 38, 537 (1997).

    Google Scholar 

  14. P. J. Carrott, J. M. W. Nabais, M. L. R. Carrott, and J. A. Pajares, Carbon, 39, 1543 (2001).

    Article  CAS  Google Scholar 

  15. B. V. Babu and R. Angira, Comput. Chem. Eng., 30, 989 (2006).

    Article  CAS  Google Scholar 

  16. R. Angira and B. V. Babu, Chem. Eng. Sci., 61, 4707 (2006).

    Article  CAS  Google Scholar 

  17. B. V. Babu and S. A. Munawar, Chem. Eng. Sci., 62, 3720 (2007).

    Article  CAS  Google Scholar 

  18. T. K. Ko, Appl. Polym. Sci., 42, 49 (1991).

    Article  Google Scholar 

  19. M. C. Paiva, P. Kotasthane, D. D. Edie, and A. A. Ogale, Carbon, 41, 399 (2003).

    Article  CAS  Google Scholar 

  20. E. Fitzer, W. Frohs, and M. Heine, Carbon, 24, 387 (1986).

    Article  CAS  Google Scholar 

  21. J. Mittal, R. B. Mathur, and O. P. Bahl, Carbon, 35, 1196 (1997).

    Article  CAS  Google Scholar 

  22. A. Gupta and I. R. Harrison, Carbon, 35, 809 (1997).

    Article  CAS  Google Scholar 

  23. J. Mittal, R. B. Mathur, and O. P. Bahl, Carbon, 35, 1713 (1997).

    Article  CAS  Google Scholar 

  24. M. Trinquecoste, J. L. Carlier, A. Derrb, P. Delhaes, and P. Chadeyron, Carbon, 34, 923 (1996).

    Article  CAS  Google Scholar 

  25. ISO 11566, “Carbon Fibre-determination of the Tensile Properties of Single-filament Specimens”, 1998a.

    Google Scholar 

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Authors and Affiliations

  1. Chemical Engineering Department, Amirkabir University of Technology (Tehran Poly Technique), Tehran, Iran

    Ramin Badrnezhad

  2. Faculty of Materials Science and Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Reza Eslami-Farsani

Authors
  1. Ramin Badrnezhad
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  2. Reza Eslami-Farsani
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Correspondence to Reza Eslami-Farsani.

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Badrnezhad, R., Eslami-Farsani, R. Modeling and differential evolution optimization of PAN carbon fiber production process. Fibers Polym 15, 1182–1189 (2014). https://doi.org/10.1007/s12221-014-1182-z

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  • DOI: https://doi.org/10.1007/s12221-014-1182-z

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