Multi-parameter data visualization by means of principal component analysis (PCA) in qualitative evaluation of various coal types

• Autorzy: Niedoba T.

Identyfikatory

DOI

10.5277/ppmp140213

• Języki publikacji: EN

Abstrakty

EN

Multi-parameter data visualization methods are a modern tool allowing to classify some analyzed objects. When it comes to grained materials, e.g. coal, many characteristics have an influence on the material quality. Besides the most obvious features like particle size, particle density or ash contents, coal has many other qualities which show significant differences between the studied types of material. The paper presents the possibility of applying visualization techniques for coal type identification and determination of significant differences between various types of coal. The Principal Component Analysis was applied to achieve this purpose. Three types of coal 31, 34.2 and 35 (according to Polish classification of coal types) were investigated, which were initially screened on sieves and subsequently divided into density fractions. Next, each size-density fraction was analyzed chemically to obtain other characteristics. It was pointed out that the applied methodology allowed to identify certain coal types efficiently, which makes it useful as a qualitative criterion for grained materials. However, it was impossible to provide such identification based on contrastive comparisons of all three types of coal. The presented methodology is a new way of analyzing data concerning widely understood mineral processing.

Słowa kluczowe

EN

principal component analysis; PCA; multi-parameter data visualization; coal; identification of data; covariance matrix; pattern recognition

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2014
• Tom: Vol. 50, iss. 2
• Strony: 575--589
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Niedoba T.

• 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. AHMED H.A.M., DRZYMALA J., Two-dimensional fractal linearization of distribution curves, Physicochemical Problems of Mineral Processing, vol. 39, pp. 129-139, 2005.
• 2. ALDRICH C., Visualization of transformed multivariate data sets with autoassociative neural networks. Pattern Recognition Letters, Volume: 19, Issue: 8, June, 1998, pp. 749-764.
• 3. ASIMOV D., The Grand Tour: A Tool for Viewing Multidimensional Data, SIAM Journal of Scientific and Statistical Computing, pp. 128-143, vol. 6, No, 1985.
• 4. ASSA J., COHEN-OR D., MILO T., RMAP: a system for visualizing data in multidimensional relevance space, Visual Computer, vol.15, no.5, pp.217-34. Publisher: Springer-Verlag, Germany, 1999.
• 5. BROZEK M., SUROWIAK A., Argument of Separation at Upgrading in the Jig, Archives of Mining Sciences, vol. 55, pp. 21-40, 2010.
• 6. BROZEK M., SUROWIAK A., Effect of Particle Shape on Jig Separation Efficiency, Physicochemical Problems of Mineral Processing, vol. 41, pp. 397-413, 2007.
• 7. BROZEK M., SUROWIAK A., The Dependence of Distribution of Settling Velocity of Spherical Particles on the Distribution of Particle Sizes and Densities, Physicochemical Problems of Mineral Processing, vol. 39, pp. 199-210, 2005.
• 8. CHATTERJEE A., DAS P.P., BHATTACHARYA S., Visualization in linear programming using parallel coordinates, Pattern Recognition, vol. 26(11), pp.1725-1736, 1993.
• 9. CHOU S.-Y., LIN S.-W., YEH C.-S., Cluster identification with parallel coordinates, Pattern Recognition Letters, vol. 20, pp.565-572, 1999.
• 10. CLEVELAND W.S., MCGILL R., The many faces of a scatterplot, Journal of the American Statistical Association, vol.79, pp.807-822. 1984
• 11. COOK D., BUJA A., CABRERA J., HURLEY C., Grand Tour and Projection Pursuit, Journal of Computational and Graphical Statistics, vol. 4, no. 3, pp. 155-172, 1995.
• 12. DRZYMALA J.: Basics of minerallurgy, Oficyna Wydawnicza Politechniki Wrocławskiej, 2009. [in Polish]
• 13. GAWENDA T., SARAMAK D., TUMIDAJSKI T., Regression models of rock materials crushing in jaw crushers, Scientific Issues of Civil Engineering and Environmental Engineering Faculty of Koszalin University of Science and Technology, series: Environmental Engineering, no 22, pp. 659-670, 2005. [in Polish]
• 14. GENNINGS C., DAWSON K.S., CARTER W.H., JR. MYERS R.H., Interpreting plots of a multidimensional dose-response surface in a parallel coordinate system. Biometrics, vol. 46, pp. 719-735, 1990.
• 15. INSELBERG A., The plane with parallel coordinates. Visual Computer, vol. 1, pp. 69-91, 1985.
• 16. JAIN A.K., MAO J., Artificial neural network for non-linear projection of multivariate data. In: Proc. IEEE Internat. Joint Conf. On Neural Networks, Baltimore, MD, vol. 3, pp. 335-340, 1992.
• 17. JAMROZ D., NIEDOBA T., Application of Observational Tunnels Method to Select Set of Features Sufficient to Identify a Type of Coal, Physicochemical Problems of Mineral Processing, vol 50(1), pp. 185-202, 2014.
• 18. JAMROZ D., Visualization of objects in multidimensional spaces, Doctoral Thesis, AGH, Kraków, 2001. [in Polish]
• 19. JAMROZ D., Application of Multidimensional Data Visualization in Creation of Pattern Recognition Systems, In: Gruca A., Czachórski T., Kozielski S. (eds.), Man-Machine, Interactions 3, AISC, Heidelberg, Springer-Verlag, vol. 242, pp. 443-450, 2014.
• 20. JAMROZ D.: Multidimensional labyrinth - multidimensional virtual reality. In: Cyran K., Kozielski S., Peters J., Stanczyk U., Wakulicz-Deja A. (eds.), Man-Machine, Interactions, AISC, Heidelberg, Springer-Verlag, pp. 445–450, 2009.
• 21. KELLY E.G., SPOTTISWOOD D.J., The Theory of Electrostatic Separations, a Review, Part I. Fundamentals, Minerals Engineering, vol. 2/1, 33-46, 1989.
• 22. KIM S.-S., KWON S., COOK D., Interactive visualization of hierarchical clusters using MDS and MST. Metrika, vol. 51, Springer-Verlag, pp. 39-51, 2000.
• 23. KRAAIJVELD M., MAO J., JAIN A.K., A nonlinear projection method based on Kohonen’s topology preserving maps. IEEE Trans. Neural Networks, vol. 6(3), pp. 548-559, 1995.
• 24. LI W., YUE H. H., VALLE-CERVANTES S., QIN S. J., Recursive PCA for adaptive process monitoring. Journal of Process Control, vol. 10, iss.: 5, pp. 471-486, 2000.
• 25. LYMAN G. J., Application of Line-Length Related Interpolation Methods to Problems in Coal Preparation – III: Two dimensional Washability Data Interpolation, Coal Preparation, vol. 13, pp. 179-195, 1993.
• 26. NIEDOBA T., Multidimensional characteristics of random variables in description of grained materials and their separation processes, Wydawnictwo Instytutu Gospodarki Surowcami Mineralnymi i Energią PAN, Kraków, 2013a [in Polish].
• 27. NIEDOBA T., Multidimensional distributions of grained materials characteristics by means of non-parametric approximation of marginal statistical density function, AGH Journal of Mining and Geoengineering, iss. 4, pp. 235-244, 2009 [in Polish].
• 28. NIEDOBA T., Statistical analysis of the relationship between particle size and particle density of raw coal, Physicochemical Problems of Mineral Processing, vol. 49, iss. 1, pp. 175-188, 2013b.
• 29. NIEDOBA T., JAMROZ D.: Visualization of multidimensional data in purpose of qualitative classification of various types of coal, Archives of Mining Sciences, vol. 58, iss. 4, pp. 1317-1333, 2013.
• 30. NIEDOBA T., SUROWIAK A., 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, pp. 3844-3854, 2012.
• 31. NIEDOBA T., Three-dimensional distribution of grained materials characteristics, in Proceedings of the XIV Balkan Mineral Processing Congress, Tuzla, Bosnia and Herzegovina, vol. 1, pp. 57-59, 2011.
• 32. OLEJNIK T., SUROWIAK A., GAWENDA T., NIEDOBA T., TUMIDAJSKI T., Multidimensional coal characteristics as basis for evaluation and adjustment of its beneficiation technology, AGH Journal of Mining and Geoengineering, vol. 34, iss. 4/1, pp. 207-216, 2010. [in Polish]
• 33. SARAMAK D., Mathematical models of particle size distribution in simulation analysis of High-pressure grinding rolls operation, Physicochemical Problems of Mineral Processing, vol. 49(1), pp. 495–512, 2013.
• 34. SARAMAK D., Technological Issues Of High-Pressure Grinding Rolls Operation In Ore Comminution Processes, Archives of Mining Sciences, vol. 56, no 3, pp. 517-526, 2011.
• 35. SNOPKOWSKI R., NAPIERAJ A., Method Of The Production Cycle Duration Time Modeling Within Hard Coal Longwall Faces, Archives of Mining Sciences, vol. 57, no. 1, pp. 121-138, 2012.
• 36. SOBOL M.G., KLEIN G., New graphics as computerized displays for human information processing. IEEE Trans. Systems Man Cybernet., vol. 19 (4), pp. 893-898, 1989.
• 37. TUMIDAJSKI T., SARAMAK D., Methods and models of mathematical statistics in mineral processing, Wydawnictwo AGH, Kraków, 2009. [in Polish]
• 38. TUMIDAJSKI T., Stochastic analysis of grained materials properties and their separation processes, Wydawnictwo AGH, Kraków, 1997 [in Polish].