Parametric identification in the problem of determining the quality of dusulfusation and deposphoration processes of Fe-C alloy

Authors

DOI:

https://doi.org/10.15587/2312-8372.2017.99130

Keywords:

parametric methods of pattern recognition, classifying rule, desulfurization, dephosphorization

Abstract

It is established that an algorithm for parametric methods of pattern recognition can be used to build a classifying rule that allows to identify a section of the duplex-melting process «cupola – electric arc furnace», which has a lower index of parametric reliability. It is shown that in this case it is sufficient to build a linear discriminant function.

The resulting classifying rule provides a high recognition rate (91.4 % and 97.1 %) of object belonging to class A (melting in cupola) and class B (melting in electric arc furnace), respectively. This allows it to be recommended for decision support systems for desulphurization and dephosphorization control in the duplex-melting process «cupola – electric arc furnace». The application of this rule opens the possibility of improving the quality of desulphurization and dephosphorization control due to the correct choice of control actions.

Additional opportunities for using the above results in industrial conditions are associated with the use of the classifying rule in decision support information systems. It can be implemented by integration of the appropriate mathematical description into the melting information control system (ICS).

Author Biography

Mourad Aouati, Central City Police Department of Constantine, Ali Mendjeli UV 01 Ilot 03 Bt H n°123, Constantine, Algeria

Chief Commissioner of Police

References

  1. Trufanov, I., Liutyi, A., Chumakov, K., Andriias, I., Kazanskaia, T., Dzhioev, V. (2010). Scientific bases of the permission of innovative problems identifications in processes automation systems of electrometallurgy of the steel and alloys. Eastern-European Journal of Enterprise Technologies, 3(10(45)), 8–23. Available: http://journals.uran.ua/eejet/article/view/2898
  2. Razzhivin, A., Sagaida, I. (2000). Informatsionnoe obespechenie sistemy avtomaticheskogo upravleniia dugovoi staleplavil'noi pech'iu po temperature metalla. Vіsnik SUDU, 3 (25), 215–220.
  3. Demin, D. (2014). Mathematical description typification in the problems of synthesis of optimal controller of foundry technological parameters. Eastern-European Journal of Enterprise Technologies, 1(4(67)), 43–56. doi:10.15587/1729-4061.2014.21203
  4. Trufanov, I., Chumakov, K., Bondarenko, A. (2005). Obshcheteoreticheskie aspekty razrabotki stohasticheskoi sistemy avtomatizirovannoi ekspertnoi otsenki dinamicheskogo kachestva proizvodstvennyh situatsii elektrostaleplavleniia. Eastern-European Journal of Enterprise Technologies, 6(2(18)), 52–58.
  5. Demin, D. (2014). Quality Control at foundries technological aspects in selection of optimal strategies for technical reequipment. Bulletin of NTU «KhPI». Series: New desicions of modern technologies, 7 (1050), 42–52.
  6. Dembovskii, V. (1989). Avtomatizatsiia liteinyh protsessov. Leningrad: Mashinostroenie, 264.
  7. Demin, D. (2010). Priniatie reshenii v protsesse upravleniia elektroplavkoi s uchetom faktorov nestabil'nosti tehnologicheskogo protsessa. Bulletin of NTU «KhPI». Series: New desicions of modern technologies, 17, 67–72.
  8. Vasenko, Yu. (2012). Technology for improved wear iron. Technology Audit and Production Reserves, 1(1(3)), 17–21. doi:10.15587/2312-8372.2012.4870
  9. Demin, D., Bozhko, A., Zraichenko, A., Nekrasov, A. (2006). Identifikatsiia chuguna dlia opredeleniia ratsional'nyh rezhimov legirovaniia. Eastern-European Journal of Enterprise Technologies, 4(1(22)), 29–32.
  10. Ponomarenko, O., Trenev, N. (2013). Computer modeling of crystallization processes as a reserve of improving the quality of pistons of ICE. Technology Audit and Production Reserves, 6(2(14)), 36–40. doi:10.15587/2312-8372.2013.19529
  11. Manikaeva, О., Arsirii, Е., Vasilevskaja, O. (2015). Development of the decision support subsystem in the systems of neural network pattern recognition by statistical information. Eastern-European Journal of Enterprise Technologies, 6(4(78)), 4–12. doi:10.15587/1729-4061.2015.56429
  12. Fraze-Frazenko, A. (2012). Algorithm of study neural network for image recognition. Technology Audit and Production Reserves, 4(1(6)), 33–34. doi:10.15587/2312-8372.2012.4781
  13. Unglert, K., Radić, V., Jellinek, A. M. (2016). Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra. Journal of Volcanology and Geothermal Research, 320, 58–74. doi:10.1016/j.jvolgeores.2016.04.014
  14. Fakhar, K., El Aroussi, M., Saidi, M. N., Aboutajdine, D. (2016). Fuzzy pattern recognition-based approach to biometric score fusion problem. Fuzzy Sets and Systems, 305, 149–159. doi:10.1016/j.fss.2016.05.005
  15. Demin, D. A. (2013). Nechetkaia klasterizatsiia v zadache postroenie modelei «Sostav – svoistvo» po dannym passivnogo eksperimenta v usloviiah neopredelionnosti. Problemy mashinostroeniya, 16 (6), 15–23. Available: http://dspace.nbuv.gov.ua/handle/123456789/80953
  16. Hartmann, K., Lezki, E., Schafer, W. (1974). Statistische Versuchsplanung und-auswertung in der Stoffwirtschaft. Leipzig, 552.
  17. Anderson, W. K. (1979). Computer-assisted studies of chemical structure and biological function (Stuper, Andrew J.; Brugger, William E.; Jurs, Peter C.). Journal of Chemical Education, 56 (12), A380. doi:10.1021/ed056pa380.4
  18. Aouati, M. (2016). Localization of vectors–patterns in the problems of parametric classification with the purpose of increasing its accuracy. Eastern-European Journal of Enterprise Technologies, 4(4(82)), 10–20. doi:10.15587/1729-4061.2016.76171

Published

2017-03-30

How to Cite

Aouati, M. (2017). Parametric identification in the problem of determining the quality of dusulfusation and deposphoration processes of Fe-C alloy. Technology Audit and Production Reserves, 2(1(34), 9–15. https://doi.org/10.15587/2312-8372.2017.99130

Issue

Section

Metallurgical Technology: Original Research