Advertisement

Petroleum Chemistry

, Volume 59, Issue 11, pp 1207–1212 | Cite as

Assessment of Resource Potential of Heavy Petroleum Residues by Data Envelopment Analysis

  • P. M. Tyukilina
  • P. E. KrasnikovEmail author
  • M. Yu. Derevyanov
  • A. A. Pimenov
  • Yu. E. Pleshivtseva
Article
  • 12 Downloads

Abstract

The resource potential of heavy petroleum residues for obtaining high-quality road asphalt has been estimated using data envelopment analysis (DEA), an effective method for processing large data arrays. The approach used has made it possible to determine the frontier of the resource potential of heavy petroleum residues, that is, to predict the quality of asphalt for any combination of the composition of residual petroleum feedstock and obtain information about the necessity of adjusting the component composition of mixtures for manufacturing the product with desired properties. Using a sample of 99 experiments, it has been shown that the highest efficiency in achieving the normative characteristics of asphalt is achieved by introducing heavy vacuum gas oil and atmospheric residue into a highly viscous vacuum residue. The feasibility has been shown of using effectively deasphalted asphalt and slop as modifying components, the optimum concentrations of which in the vacuum residue are 20 and 40 wt %, respectively.

Keywords:

data envelopment analysis (DEA) heavy petroleum residues road asphalt modifier resource potential physicochemical properties of asphalt chemical-group composition 

Notes

FUNDING

This work was supported in part by the Ministry of Education and Science of the Russian Federation (project part of the state assignment, project no. 10.3260.2017/4.6)

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest requiring disclosure in this paper.

REFERENCES

  1. 1.
    Yu. E. Pleshivtseva, M. Yu. Derevyanov, D. V. Kashirskikh, et al., Neft. Khoz., No. 8, 104 (2018).Google Scholar
  2. 2.
    P. M. Tyukilina, V. N. Mel’nikov, V. A. Tyshchenko, et al., Khim. Tekhnol. Topl. Masel, No. 5, 13 (2015).Google Scholar
  3. 3.
    W. W. Cooper and L. M. Seiford, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software (Kluwer Academic, Boston, 2000).Google Scholar
  4. 4.
    G. Yu. Chernyshova and R. N. Kovalev, Fundam. Issled., No. 8, 453 (2017).Google Scholar
  5. 5.
    A. N. Lissitsa and T. S. Babicheva, Disc. Pap., No. 50 (2003). http://nbn-resolving.de/urn:nbn:de:gbv:3:2-23263.Google Scholar
  6. 6.
    E. Behzadfar and S. G. Hatzikiriakos, Fuel, 116, 578 (2014).CrossRefGoogle Scholar
  7. 7.
    P. E. Krasnikov, M. M. Gavrilov, K. A. Efimenko, et al., Pet. Chem. 58, 646 (2018).CrossRefGoogle Scholar
  8. 8.
    A. A. Gureev, Petroleum-Based Binder Materials (Nedra, Moscow, 2018) [in Russian].Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  • P. M. Tyukilina
    • 1
  • P. E. Krasnikov
    • 1
    Email author
  • M. Yu. Derevyanov
    • 1
  • A. A. Pimenov
    • 1
  • Yu. E. Pleshivtseva
    • 1
  1. 1.Samara State Technical UniversitySamaraRussia

Personalised recommendations