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Application of the observational tunnels method to select a set of features sufficient to identify a type of coal

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Języki publikacji
EN
Abstrakty
EN
Coal is a material which has many features deciding about its quality. Among them, the decisive ones are mainly ash contents, sulfur contents and combustion heat. The paper presents the investigation of coal characteristics of three selected coal types in the context of their energetic value. For this purpose samples were collected from three different Polish mines: coal types 31, 34.2 and 35 (Polish classification of coals). Each of these materials was separated into particle size fractions (9 fractions) and then into 8 density fractions by separation in heavy liquids. For each size-density fractions obtained in this way, chemical analyses were performed which allowed for determination of such features as combustion heat, sulfur contents, ash contents, volatile parts contents and analytical moisture. Altogether, seven dimensions of grained material characteristics were obtained. The data prepared in this way was subsequently analyzed for correlation with the purpose of determining significant relations between investigated features. It was stated that the most correlated coal features are density, combustion heat, ash contents and volatile parts contents. For multidimensional analysis and identification of coal type, the modern image visualization technique, the Observational Tunnels Method, was applied. After performing seven-dimensional analysis aimed at the proper recognition of coal type, it was decided to determine the minimum amount of random variables, which describe a particular material in order to identify its type. It was stated that the crucial coal identification parameter is “analytical moisture”. Due to existing correlation between individual features, three of them were selected for testing: analytical moisture, sulfur contents and volatile parts contents. On the basis of the obtained images, it was stated that it was possible to obtain a view with the data concerning each type of coal being located in other part of the space. Subsequently, it was checked if a similar result is possible when the parameter “volatile parts contents” is replaced with highly correlated parameters “combustion heat” and “ash contents”. In both cases the exchange of these variables did not produce good enough results. This can be explained by a different scale of empirical data making it impossible to obtain a clear multidimensional image for which all three types of coal would be located in other parts of space. However, it was proved that the modern graphical and computer methods can be successfully applied to identify the types of particulate materials.
Rocznik
Strony
185−202
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Computer Science, al. Mickiewicza 30, 30-059 Krakow, Poland
autor
  • AGH University of Science and Technology, Faculty of Mining and Geoengineering, Department of Environmental Engineering and Mineral Processing, al. Mickiewicza 30, 30-059 Krakow
Bibliografia
  • 1. ALDRICH C., 1998, Visualization of transformed multivariate data sets with autoassociative neural networks, Pattern Recognition Letters, vol. 19, issue: 8, 749–764.
  • 2. ASSA J., COHEN-OR D., MILO T., 1997, Displaying data in multidimensional relevance space with 2D visualization maps, Proceedings. Visualization '97, 127–134. New York, NY, IEEE.
  • 3. ASSA J., COHEN-OR D., MILO T., 1999, RMAP: a system for visualizing data in multidimensional relevance space, Visual Computer, vol. 15, no. 5, 217–234.
  • 4. CHATTERJEE A., DAS P.P., BHATTACHARYA S., 1993, Visualization in linear programming using parallel coordinates, Pattern Recognition 26(11), 1725–1736.
  • 5. CHOU S.Y., LIN S.W., YEH C.S., 1999, Cluster identification with parallel coordinates, Pattern Recognition Letters 20, 565–572.
  • 6. COOK D., BUJA A., CABRERA J., HURLEY C., 1995, Grand Tour and Projection Pursuit, Journal of Computational and Graphical Statistics, vol. 4, no. 3, 155–172.
  • 7. HEIKE H., 2000, Exploring categorical data: interactive mosaic plots, Metrika 51, 11–26.
  • 8. HURLEY C., BUJA A., 1990, Analyzing high-dimensional data with motion graphics, SIAM Journal on Scientific & Statistical Computing, vol. 11, no. 6, 1193–1211.
  • 9. JAMROZ D., 2009, Multidimensional labyrinth – multidimensional virtual reality. In: Cyran K., Kozielski S., Peters J., Stanczyk U., Wakulicz-Deja A. (eds.), Man-Machine, Interactions, AISC, vol. 59, 445–450. Springer-Verlag, Berlin Heidelberg, Germany.
  • 10. JAMROZ D., 2001, Visualization of objects in multidimensional spaces. Ph.D. Thesis, AGH, University of Science and Technology, Cracow, Poland.
  • 11. KIM S., KWON S., COOK D., 2000, Interactive visualization of hierarchical clusters using MDS and MST, Metrika 51, 39-51, Springer-Verlag.
  • 12. KRAAIJVELD M., MAO J., JAIN A.K., 1995, A nonlinear projection method based on Kohonen’s topology preserving maps, IEEE Trans. Neural Networks 6(3), 548-559.
  • 13. LI W., YUE H.H., VALLE-CERVANTES S., QIN S.J. , 2000, Recursive PCA for adaptive process monitoring, Journal of Process Control, vol. 10, issue: 5, 471–486.
  • 14. Lyman G. J., 1993, Application of Line-Length Related Interpolation Methods to Problems in Coal Preparation – III: Two dimensional Washability Data Interpolation, Coal Preparation, vol. 13, 179–195.
  • 15. NIEDOBA T., 2013, Statistical analysis of the relationship between particle size and particle density of raw coal, Physicochemical Problems of Mineral Processing, vol. 49, iss. 1, 175–188.
  • 16. NIEDOBA T., SUROWIAK A. , 2012, Type of coal and multidimensional description of its composition with density and ash contents taken into consideration, in Proceedings of the XXVI International Mineral Processing Congress, vol. 1, New Delhi, 3844–3854.
  • 17. NIEDOBA T., 2011, Three-dimensional distribution of grained materials characteristics, in Proceedings of the XIV Balkan Mineral Processing Congress, Tuzla, Bosnia and Herzegovina, vol. 1, 57–59.
  • 18. NIEDOBA T., 2009, Wielowymiarowe rozkłady charakterystyk materiałów uziarnionych przy zastosowaniu nieparametrycznych aproksymacji funkcji gęstości rozkładów brzegowych, Górnictwo i Geoinżynieria, iss. 4, 235–244.
  • 19. NIEDOBA T., JAMROZ D., 2013, Visualization of multidimensional data in purpose of qualitative classification of various types of coal, Archives of Mining Sciences (paper in printing).
  • 20. OLEJNIK T., SUROWIAK A., GAWENDA T., NIEDOBA T., TUMIDAJSKI T., 2010, Wielowymiarowe charakterystyki węgli jako podstawa do oceny i korekty technologii ich wzbogacania, Górnictwo i Geoinżynieria, vol. 34, iss. 4/1, 207–216.
  • 21. STANISZ A., 2007, Przystępny kurs statystyki w oparciu o program Statistica PL na przykładach z medycyny, tom III: Analizy wielowymiarowe, Wyd. Statsoft, Kraków.
  • 22. TUMIDAJSKI T., SARAMAK D., 2009, Metody i modele statystyki matematycznej w przeróbce surowców mineralnych, Wydawnictwo AGH, Kraków.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-df1935bc-12f3-425a-9717-18b5ca11e401
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