Improving the accuracy of classifying rules for controlling the processes of deculfuration and dephosphorization of Fe-C melt

Authors

  • Mourad Aouati Central City Police Department of Constantine, Ali Mendjeli UV 01 Ilot 03 Bt H n°123, Constantine, Algeria; National Technical University «Kharkiv Polytechnic Institute», 2, Kyrpychova str., Kharkiv, Ukraine, 61002, Algeria https://orcid.org/0000-0003-2744-3592

DOI:

https://doi.org/10.15587/2312-8372.2019.169696

Keywords:

chemical-technological system, Fe-C melt, classifying rules, statistical classification, discriminant function

Abstract

The object of research is the process of functioning of the chemical-technological system of two series-connected units, designed to produce Fe-C melt. This process is evaluated on the basis of the classification rules, which makes it possible to identify that component of the system, according to which the deviations of the operating mode from the normal by chemical analysis of the content of sulfur and phosphorus are revealed. Such consideration allows determining the completeness of the desulfurization and dephosphorization processes of the Fe-C melt. One of the most problematic places is the lack of systematized data on the procedures for determining possible deviations from the normal mode of functioning of the aggregates in terms of the completeness of the desulfurization and dephosphorization processes.

In the course of research, the methods of parametric classification were used, which allow one to obtain an analytical description of the discriminant function and, based on a comparison of its values at a given point in the space of factor-attributes with a threshold value, assign an object to one of the classes. In this research, each of the classes characterizes an aggregate of a chemical-technological system for the content of sulfur and phosphorus in the Fe–C melt.

The obtained results allow to state that it is possible to construct a broken curve separating classes using the methods of parametric classification. This is due to the fact that the proposed five-step procedure for selecting the input data area makes it possible to remove one of the restrictions on the use of parametric classification methods, namely, the requirement of equality of covariance matrices. The proposed procedure has a number of features, in particular, the choice of the input data area for calculating the coefficients of the discriminant function and threshold values is determined through the vertices of the plans for the full factorial experiment. This ensures the possibility of obtaining one hundred percent accuracy of classification in areas corresponding to its plan. Compared to the results of the construction of classification rules for the total sample of data, that is, for all points of the space of factor-attributes, and not only for points of the chosen plan, this does not have significant advantages. But from the point of view of obtaining the form of a separating curve other than linear, this can be beneficial if the input data samples are not well separated.

Author Biography

Mourad Aouati, Central City Police Department of Constantine, Ali Mendjeli UV 01 Ilot 03 Bt H n°123, Constantine, Algeria; National Technical University «Kharkiv Polytechnic Institute», 2, Kyrpychova str., Kharkiv, Ukraine, 61002

Chief Commissioner of Police;

Postgraduate Student

Department of Foundry

References

  1. 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. doi: http://doi.org/10.15587/2312-8372.2017.99130
  2. Grachev, V. A., Kuznecov, B. L., Bochkarev, V. Е., Venger, V. V. (1988). Metallurgiia plavki chuguna v dugovoi pechi. Liteinoe proizvodstvo, 2, 19–21.
  3. Shulte, Iu. A. (1983). Proizvodstvo otlivok iz stali. Kyiv; Doneck: Vishcha shkola. Golovnoe izd-vo, 184.
  4. Pavlenko, V. D., Fomin, A. A. (2001). Povyishenie tochnosti postroeniya reshayuschego pravila v metodah statisticheskoy klassifikatsii. Elektronnoe modelirovanie, 23 (4), 61–68.
  5. Vasenko, Y. (2012). Technology for improved wear iron. Technology Audit and Production Reserves, 1 (1 (3)), 17–21. doi: http://doi.org/10.15587/2312-8372.2012.4870
  6. Ponomarenko, O. I. Trenev, N. S. (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: http://doi.org/10.15587/2312-8372.2013.19529
  7. Ignaszaka, Z., Popielarskia, P., Krawiecb, K. (2007). Contribute to quantitative identification of casting defects based on computer analysis of X-ray images. Archives of foundry engineering, 7 (4 (18/4)), 89–94.
  8. Lin, Z., Lyu, M. R., King, I. (2011). MatchSim: a novel similarity measure based on maximum neighborhood matching. Knowledge and Information Systems, 32 (1), 141–166. doi: http://doi.org/10.1007/s10115-011-0427-z
  9. Arsirii, Е., Manikaeva, О., Vasilevskaia, 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)), 4–15. doi: http://doi.org/10.15587/1729-4061.2015.56429
  10. Fraze-Frazenko, A. (2012). Algorithm of study neural network for image recognition. Technology Audit and Production Reserves, 4 (1 (6)), 33–34. doi: http://doi.org/10.15587/2312-8372.2012.4781
  11. Fertsev, A. A. (2012). Uskorenie obucheniya neyronnoy seti dlya raspoznavaniya izobrazheniy s pomoschyu tehnologii NVidia Cuda. Vestnik Samarskogo gosudarstvennogo tehnicheskogo universiteta, 1 (26), 183–191. doi: http://doi.org/10.14498/vsgtu990
  12. 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: http://doi.org/10.1016/j.jvolgeores.2016.04.014
  13. 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: http://doi.org/10.1016/j.fss.2016.05.005
  14. Perova, I. G. (2014). Adaptive treatment of these mediсo-biological researches by methods of computational intelligence. Eastern-European Journal of Enterprise Technologies, 1 (4 (67)), 24–28. doi: http://doi.org/10.15587/1729-4061.2014.21202
  15. 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: http://doi.org/10.15587/1729-4061.2016.76171

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Published

2018-12-31

How to Cite

Aouati, M. (2018). Improving the accuracy of classifying rules for controlling the processes of deculfuration and dephosphorization of Fe-C melt. Technology Audit and Production Reserves, 2(3(46), 10–18. https://doi.org/10.15587/2312-8372.2019.169696

Issue

Vol. 2 No. 3(46) (2019): Chemical Engineering

Section

Measuring Methods in Chemical Industry: Original Research

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Copyright (c) 2019 Mourad Aouati

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