Skip to main content
Log in

A novel system dynamics model for forecasting naphtha price

  • Polymer, Industrial Chemistry
  • Published:
Korean Journal of Chemical Engineering Aims and scope Submit manuscript

Abstract

Fluctuations in naphtha price are directly related to the profit of petrochemical companies. Thus, forecasting of naphtha price is becoming increasingly important. To respond to this need, a naphtha crack (the price gap between naphtha and crude oil) forecasting model is developed herein. The objective of this study was to design a reasonable forecasting model that is immediately available and can be used to develop various naphtha supply strategies. However, it is very difficult to forecast a price value with a high accuracy. Therefore, the proposed model focuses not on the price value but on the direction of the crack. These considerations are vital to a company’s decision-making process. In addition, a system dynamics model that considers causal relations is proposed. It was developed based on heuristics, statistical analysis, seasonal effects, and relationships between factors that affect naphtha price, and it exhibits an accuracy rate of 84%-95% in forecasting of the naphtha crack three months in advance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. G. Dooley and H. Lenihan, Resour. Policy, 30, 208 (2005).

    Article  Google Scholar 

  2. J.-H. Ryu, Korean J. Chem. Eng., 30, 27 (2013).

    Article  CAS  Google Scholar 

  3. M. Lee and J. Kim, Korean J. Chem. Eng., 34, 1604 (2017).

    Article  CAS  Google Scholar 

  4. M. Kim and J. Kim, Int. J. Hydrog. Energy, 42, 3899 (2017).

    Article  CAS  Google Scholar 

  5. P.-F. Pai and C.-S. Lin, Omega, 33, 497 (2005).

    Article  Google Scholar 

  6. J.-M. Brault, R. Labib, M. Perrier and P. Stuart, Can. J. Chem. Eng., 89, 901 (2011).

    Article  CAS  Google Scholar 

  7. X.-G. Zhou, L.-H. Liu, W.-K. Yuan and J. L. Hudson, Can. J. Chem. Eng., 74, 638 (1996).

    Article  CAS  Google Scholar 

  8. S. Mandal and A. K. Jana, Int. J. Hydrog. Energy, 38, 1244 (2013).

    Article  CAS  Google Scholar 

  9. L. M. Ochoa-Estopier, M. Jobson and R. Smith, Comput. Chem. Eng., 59, 178 (2013).

    Article  CAS  Google Scholar 

  10. B. Szkuta, L. Sanabria and T. Dillon, IEEE Trans. Power Syst., 14, 851 (1999).

    Article  Google Scholar 

  11. R. Gareta, L. M. Romeo and A. Gil, Energy Conv. Manag., 47, 1770 (2006).

    Article  Google Scholar 

  12. R. Jammazi and C. Aloui, Energy Econ., 34, 828 (2012).

    Article  Google Scholar 

  13. A. J. Conejo, M. A. Plazas, R. Espinola and A. B. Molina, IEEE Trans. Power Syst., 20, 1035 (2005).

    Article  Google Scholar 

  14. P. Visetsripong, P. Sooraksa, P. Luenam and W. Chaimongkol, SICE Annual Conference, 659–663 (2008).

    Google Scholar 

  15. X. Yan and N. A. Chowdhury, Int. J. Electr. Power Energy Syst., 53, 20 (2013).

    Article  Google Scholar 

  16. J. Myklebust, A. Tomasgard and S. Westgaard, OPEC Energy Review, 34, 82 (2010).

    Article  Google Scholar 

  17. N. Salehnia, M. A. Falahi, A. Seifi and M. H. M. Adeli, J. Nat. Gas Sci. Eng., 14, 238 (2013).

    Article  Google Scholar 

  18. K. Tak, J. Kim, H. Kwon, J. H. Cho and I. Moon, Korean J. Chem. Eng., 33, 1999 (2016).

    Article  CAS  Google Scholar 

  19. D. Manca, Comput. Chem. Eng., 57, 3 (2013).

    Article  CAS  Google Scholar 

  20. R. Rasello and D. Manca, Comput. Aided Chem. Eng., 433 (2014).

    Google Scholar 

  21. K. J. Lee, T. H. Lee, L.-H. Kim and Y. K. Yeo, Korean J. Chem. Eng., 28, 1505 (2011).

    Article  CAS  Google Scholar 

  22. T. H. Lee, K. J. Lee, B.W. Jo, L. H. Kim and Y. K. Yeo, Korean J. Chem. Eng., 28, 1331 (2011).

    Article  CAS  Google Scholar 

  23. C. Sung, H. Kwon, J. Lee, H. Yoon and I. Moon, Comput. Aided Chem. Eng., 145 (2012).

    Google Scholar 

  24. B. Lyu, H. Kwon, J. Lee, H. Yoon, J. Jin and I. Moon, Comput. Aided Chem. Eng., 829 (2014).

    Google Scholar 

  25. H. Kwon, B. Lyu, K. Tak, J. Lee, J. H. Cho and I. Moon, Comput. Chem. Eng., 84, 226 (2016).

    Article  CAS  Google Scholar 

  26. H. Kwon, K. Tak, J. H. Cho, J. Kim and I. Moon, Ind. Eng. Chem. Res., 56, 1267 (2017).

    Article  CAS  Google Scholar 

  27. X. Tang, B. Zhang, M. Höök and L. Feng, Energy, 35, 3097 (2010).

    Article  Google Scholar 

  28. A. Aslani, P. Helo and M. Naaranoja, Appl. Energy, 113, 758 (2014).

    Article  Google Scholar 

  29. M. A. Rendon-Sagardi, C. Sanchez-Ramirez, G. Cortes-Robles, G. Alor-Hernandez and M. G. Cedillo-Campos, Appl. Energy, 123, 358 (2014).

    Article  Google Scholar 

  30. H. Qudrat-Ullah, Energy, 59, 285 (2013).

    Article  Google Scholar 

  31. R. Rehan, M. A. Knight, A. J. A. Unger and C. T. Haas, Tunn Undergr Sp Tech, 39, 116 (2014).

    Article  Google Scholar 

  32. O. Erdem, E. Ceyhan and Y. Varli, Physica A: Statistical Mechanics and its Applications, 414, 274 (2014).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Il Moon.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lyu, B., Kwon, H. & Moon, I. A novel system dynamics model for forecasting naphtha price. Korean J. Chem. Eng. 35, 1033–1044 (2018). https://doi.org/10.1007/s11814-017-0235-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11814-017-0235-6

Keywords

Navigation