Journal of the Korean Chemical Society (대한화학회지)

Korean Chemical Society (대한화학회)
권호 표지
DOI QR코드

DOI QR Code

A Kinetic Monte Carlo Simulation of Individual Site Type of Ethylene and α-Olefins Polymerization

  • Zarand, S.M. Ghafelebashi (Polymer Research Group, Iran Petrochemical Research and Technology Company) ;
  • Shahsavar, S. (Iran Polymer and Petrochemical Institute) ;
  • Jozaghkar, M.R. (Iran Polymer and Petrochemical Institute)
  • Received : 2018.02.10
  • Accepted : 2018.04.26
  • Published : 2018.06.20
Download PDF
⟨ Previous Next ⟩

Abstract

The aim of this work is to study Monte Carlo simulation of ethylene (co)polymerization over Ziegler-Natta catalyst as investigated by Chen et al. The results revealed that the Monte Carlo simulation was similar to sum square error (SSE) model to prediction of stage II and III of polymerization. In the case of activation stage (stage I) both model had slightly deviation from experimental results. The modeling results demonstrated that in homopolymerization, SSE was superior to predict polymerization rate in current stage while for copolymerization, Monte Carlo had preferable prediction. The Monte Carlo simulation approved the SSE results to determine role of each site in total polymerization rate and revealed that homopolymerization rate changed from site to site and order of center was different compared to copolymerization. The polymer yield was reduced by addition of hydrogen amount however there was no specific effect on uptake curve which was predicted by Monte Carlo simulation with good accuracy. In the case of copolymerization it was evolved that monomer chain length and monomer concentration influenced the rate of polymerization as rate of polymerization reduced from 1-hexene to 1-octene and increased when monomer concentration proliferate.

Keywords

References

  1. Andrady, A. L. Plastics and the Environment; John Wiley & Sons: 2003.
  2. Peacock, A. Handbook of Polyethylene: Structures: Properties, and Applications; CRC Press: 2000.
  3. Ghafelebashi Zarand, S. M.; Mortazavi, S. M. M. Journal of Petroleum Science and Technology 2012, 2, 12.
  4. Salami-Kalajahi, M.; Haddadi-Asl, V.; Najafi, M.; Ghafelebashi Zarand, S. M. e-Polymers 2008, 004, 1.
  5. Liu, Y.; Zhang, B.; Fu, Z.; Fan, Z. Macromol. Res. 2017, 1.
  6. Kissin, Y. V.; Mink, R. I.; Nowlin, T. E.; Brandolini, A. J. Top. Catal. 1999, 7, 69. https://doi.org/10.1023/A:1019199330327
  7. Kissin, Y. V. J. Polym. Sci. Part A Polym. Chem. 2003, 41, 1745. https://doi.org/10.1002/pola.10714
  8. Kissin, Y. V. J. Polym. Sci. Part A Polym. Chem. 1995, 33, 227. https://doi.org/10.1002/pola.1995.080330205
  9. Skomorokhov, V. B.; Zakharov, V. A.; Kirillov, V. A. Macromol. Chem. Phys. 1996, 197, 1615.
  10. Barabanov, A. A.; Zakharov, V. A.; Sukulova, V. V. J. Organomet. Chem. 2015, 798, 292. https://doi.org/10.1016/j.jorganchem.2015.05.042
  11. Kissin, Y. V.; Mirabella, F. M.; Meverden, C. C. J. Polym. Sci. Part A Polym. Chem. 2005, 43, 4351. https://doi.org/10.1002/pola.20875
  12. Salami-Kalajahi, M.; Haddadi-Asl, V.; Najafi, M.; Ghafelebashi Zarand, S. M. e-Polymers 2008, 8, 29.
  13. Gemoets, F.; Zhang, M.; Karjala, T. W.; Kolthammer, B. W. S. Macromol. React. Eng. 2010, 4, 109. https://doi.org/10.1002/mren.200900055
  14. Hutchinson, R. A.; Chen, C. M.; Ray, W. H. J. Appl. Polym. Sci. 1992, 44, 1389. https://doi.org/10.1002/app.1992.070440811
  15. Alshaiban, A.; Soares, J. B. P. Macromol. React. Eng. 2012, 6, 265. https://doi.org/10.1002/mren.201200002
  16. Yao, K. Z.; Shaw, B. M.; Kou, B.; McAuley, K. B.; Bacon, D. W. Polym. React. Eng. 2003, 11, 563. https://doi.org/10.1081/PRE-120024426
  17. Chen, K.; Mehdiabadi, S.; Liu, B.; Soares, J. B. P. Macromol. React. Eng. 2016.
  18. Kissin, Y. V.; Mink, R. I.; Nowlin, T. E. J. Polym. Sci. Part A Polym. Chem. 1999, 37, 4255. https://doi.org/10.1002/(SICI)1099-0518(19991201)37:23<4255::AID-POLA2>3.0.CO;2-H
  19. Chen, K.; Mehdiabadi, S.; Liu, B.; Soares, J. B. P. Macromol. React. Eng. 2016, 1.
  20. Bruns, W.; Motoc, I.; O'Driscoll, K. F. Monte Carlo Applications in Polymer Science; Springer Science & Business Media: 2012. Vol. 27.
  21. Lowry, G. G. Markov Chains and Monte Carlo Calculations in Polymer Science; Marcel Dekker: 1970.
  22. Shao, J.; Tang, W.; Xia, R.; Feng, X.; Chen, P.; Qian, J.; Song, C. Macromol. Res. 2015, 23, 1042. https://doi.org/10.1007/s13233-015-3136-8
  23. Beigzadeh, D. Macromol. Theory Simul. 2003, 12, 174. https://doi.org/10.1002/mats.200390011
  24. He, J.; Zhang, H.; Chen, J.; Yang, Y. Macromolecules 1997, 30, 8010. https://doi.org/10.1021/ma9614858
  25. Fluendly, M. Markov Chains and Monte Carlo Calculations in Polymer Science; Marcel Dekker: New York, 1970.
  26. Gillespie, D. T. J. Comput. Phys. 1976, 22, 403. https://doi.org/10.1016/0021-9991(76)90041-3
  27. Zhenghon, L. U. O.; Zhikai, C. A. O.; Yaotang, S. U. Chin. J. Chem. Eng. 2006, 14, 194.
  28. Al'Harthi, M.; Soares, J. B. P.; Simon, L. C. Macromol. Mater. Eng. 2006, 291, 993. https://doi.org/10.1002/mame.200600155
  29. Khorasani, M. M.; Saeb, M. R.; Mohammadi, Y.; Ahmadi, M. Chem. Eng. Sci. 2014, 111, 211. https://doi.org/10.1016/j.ces.2014.02.019
  30. Kalajahi, M. S.; Najafi, M.; Asl, V. H. Application of Monte Carlo simulation method to polymerization kinetics over Ziegler-Natta catalysts. Int. J. Chem. Kinet. 2009, 41, 45. https://doi.org/10.1002/kin.20362