Dynamic weight-based multi-features fuzzy fusion for recovery-decision of waste lubrication oil

Article
  • 7 Downloads

Abstract

Upon contaminated to a certain extent, the lubrication oil should be changed for recovery. An effective decision of the waste oil recovery process is in generally affected by factors including the vary testing indices are, contamination level and other limited conditions,called muti-features. To crack this nut, a integrated method was proposed to obtain the dynamical weights to be fused in the DS frame. Firstly, the fuzzy analytic hierarchy process (FAHP) method was proposed to solve the multi-features weights distribution by the decision makers, and the Change-weight method was used to dynamically adjust their weights by the real status, rather than the fixed weights distribution; Further, the schemes supporting information corresponding to every feature is evaluated by each decision maker, and their weights are dynamically calculated too by the joint application of technique for order preference by similarity to ideal solution (TOPSIS). The two types of dynamic weights are regarded as the basic probability assignment (BPA) to fuse the assessment information integrated by the DS theory of evidence. An example of the waste oil recovery-decision is presented to illustrate the application of the method. The effectiveness of the proposed method is validated by the example.

Keywords

Oil recovery-decision Multi-features fusion DS theory of evidence Dynamic weight 

Notes

Acknowledgements

Thanks to the innovative team of Chongqing university waste oil reuse technologies and equipment and the authors of references. This research was funded by the projects: Chongqing Education Committee Science & Technology protect (KJ2011706), Chongqing university innovation team project (KJTD201019).

References

  1. 1.
    Balat, H.: Prospects of biofuels for a sustainable energy future: A critical assessment. Energy Educ. Sci. Technol. Part A 24, 85–111 (2010)Google Scholar
  2. 2.
    Kalipci, E., Ozdemir, C., Sahinkaya, S.: Evaluation of manageable biological waste utilization of Konya in terms of environment and energy recovery. Energy Educ. Sci. Technol. Part A 27, 35–42 (2011)Google Scholar
  3. 3.
    Jia, R.Q., Wang, L.P.: The research situation and main expectation on the technology of hydraulic contamination control. Hydraul. Pneum. Seals 1, 38–40 (2004)Google Scholar
  4. 4.
    Demirbas, A.H.: Inexpensive oil and fats feedstocks for production of biodiesel. Energy Educ. Sci. Technol. Part A 23, 1–13 (2009)Google Scholar
  5. 5.
    Kirtay, E.: The role of renewable energy sources in meeting Turkey’s electrical energy demand. Energy Educ. Sci. Technol. Part A 23, 15–30 (2009)Google Scholar
  6. 6.
    Kan, A.: General characteristics of waste management: A review. Energy Educ. Sci. Technol. Part A 23, 55–69 (2009)Google Scholar
  7. 7.
    Demirbas, A.: Social, economic, environmental and policy aspects of biofuels. Energy Educ. Sci. Technol. Part B 2, 5–109 (2010)Google Scholar
  8. 8.
    Hazar, H., Oner, C., Nursoy, M.: Effects of CrN coating of cylinders on engine performance. Energy Educ. Sci. Technol. Part A 23, 71–85 (2009)Google Scholar
  9. 9.
    Saidur, R., Lai, Y.K.: Parasitic energy savings in engines using nanolubricants. Energy Educ. Sci. Technol. Part A 26, 61–74 (2010)Google Scholar
  10. 10.
    Liu, H.W., Wang, G.J.: Multi-criteria decision-making methods based on intuitionistic fuzzy sets. Eur. J. Oper. Res. 179, 220–233 (2007)CrossRefMATHGoogle Scholar
  11. 11.
    Guo, C.X., Guo, H.H.: Approach of multiple attribute group decision making with different forms of preference information. Syst. Eng. Electron. 27, 63–65 (2005)Google Scholar
  12. 12.
    Jousselme, A.L., Grenier, D., Bosse, E.: A new distance between two bodies of evidence. Inf. Fus. 2, 91–101 (2001)CrossRefGoogle Scholar
  13. 13.
    Liao, C.J., Huang, X.Y., Chai, Y.: A study on the system of decision-making for vehicle collision avoidance based on information fusion. J. Syst. Simul. 16, 1589–1592 (2004)Google Scholar
  14. 14.
    Wang, Y.M.: The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees. Eur. J. Oper. Res. 175, 35–66 (2006)CrossRefMATHGoogle Scholar
  15. 15.
    Beynon, M.: A method of aggregation in DS/AHP for group decision making with the nonequivalent importance of individuals in the group. Comput. Oper. Res. 32, 1881–1896 (2005)CrossRefMATHGoogle Scholar
  16. 16.
    Yao, S., Guo, Y.J.: An improved method of aggregation in DS/AHP for multi-criteria group decision-making based on distance measure. Control Decis. 25, 894–897 (2010)MathSciNetGoogle Scholar
  17. 17.
    Desch, R.G., Kerre, E.: On the relationship between some extensions of fuzzy set theory. Fuzzy Sets Syst. 33, 227–235 (2003)MathSciNetMATHGoogle Scholar
  18. 18.
    Yang, T., Zuo, R.: A method of multi-attribute group decision making based on fuzzy distance and evidence theory. Value Eng. 7, 8–11 (2009)Google Scholar
  19. 19.
    Liu, Y.Z., Jiang, Y.C., Lin, W.L.: Adaptive group decision making method based on fuzzy distance and neural network. J. Syst. Eng. 23, 28–34 (2008)MATHGoogle Scholar
  20. 20.
    Chen, J.Z., Xu, J.P.: TOPSIS based interactive multi-attributes group decision making method and its application. Syst. Eng. Electron. 23, 811–813 (2008)MathSciNetMATHGoogle Scholar
  21. 21.
    Chen, X., Ma, L.H., Chen, Y.: Study on the assessment level of experts based on ideal point of linguistic assessment matrices. J. Northeaster Univ. 29, 1362–1365 (2008)Google Scholar
  22. 22.
    Jia, Z.W., Chen, T.R., Li, Y.H.: Target recognition based on multi-sensor information fusion. Syst. Eng. Electron. 25, 276–281 (2010)Google Scholar
  23. 23.
    Sylviele, H.M., Isabelle, B., Vidal-Madjar, D.: Application of Dempster Shafer evidence theory to unsupervised classification in multi-source remote sensing. IEEE Trans. Geosci. Remote Sens. 35, 1015–1031 (1997)Google Scholar
  24. 24.
    Beynon, M., Curry, B., Morgan, P.: The Dempster-Shafer theory of evidence: An alternative approach to multi-criteria decision modeling. OMEGA 28, 37–50 (2000)CrossRefGoogle Scholar
  25. 25.
    Ren, H.W., Deng, F.Q.: Research on data fusion fault diagnosis method based Dempster Shafer evidential theory. Syst. Eng. Electron. 27, 471–473 (2005)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Chongqing Technology and Business University, Manufacturing Equipment Mechanism Design and Control, Key Lab of ChongqingChongqingChina

Personalised recommendations