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Factor Analysis and Mathematical Modeling in Determining the Quality of Coal

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Warianty tytułu
PL
Analiza czynnikowa i modelowanie matematyczne w określeniu jakości węgla
Języki publikacji
EN
Abstrakty
EN
The separation of coal material of three types of coals originating from three various Polish hard coal mines (types 31, 34.2 and 35, according to Polish nomenclature, which were steam coal, semi-coking coal and coking coal) into particle size fractions and then into particle density fractions was done and then the following parameters were measured for each particle size-density fraction: combustion heat, ash contents, sulfur contents, volatile parts contents, analytic moisture. In this way a 7-dimensional vector of data was created. Using methods of factor analysis the important features of coal were selected, which decide about their membership to individual types. To evaluate the appropriateness of the applied method the Bartlett’s sphericity test as well coefficient of Kaiser-Mayer-Olkin (KMO) were used. To select important factors the Kaiser criterion and Cattell’s scree test were used. The obtained results were compared with the results obtained in previous works by means of observation tunnels method. The results showed which particular features are crucial to define the type of coal what is also important to select appropriate method of its enrichment. Furthermore, the construction of a mathematical model presenting the relations between these properties and particle size and density is presented. Because of the fact that particles of certain size or density may occur in neighboring fractions three sorts of relations were examined basing on regression analysis.The analysis was conducted for all three coal types. Because of the fact that the models contain various amounts of independent variables R2 coefficient, mean squared error (MSE) and Mallow’s statistics Cp were applied to evaluate and compare obtained results.
PL
Wykonano rozdział trzech typów węgla o różnych charakterystykach, pochodzących z trzech różnych kopalni węgla kamiennego w Polsce (typy 31, 34.2 oraz 35, według Polskich norm, którymi były węgiel energetycznym, pół-koksujący oraz koksujący) na klasy ziarnowe a następnie na frakcje gęstościowe. Dla każdej otrzymanej w ten sposób frakcji wielkościowo-gęstościowej zmierzono następujące parametry: ciepło spalania, zawartość popiołu, zawartość siarki, zawartość części lotnych, wilgotność analityczna. W ten sposób otrzymano siedmiowymiarowy wektor danych. Za pomocą analizy czynnikowej wybrano istotne cechy węgla, które decydują o jego przynależności do określonego typu węgla. Aby ocenić prawidłowość zastosowanej metody wykorzystano test sferyczności Bartletta oraz współczynnik Kaisera-Mayera-Olkina (KMO). Otrzymane wyniki porównano z wynikami otrzymanymi w poprzednich pracach, które uzyskano metodą tuneli obserwacyjnych. Wyniki pokazały, które cechy węgla są niezbędne do określenia typu węgla, co wpływa na dobór odpowiedniej metody jego wzbogacania. Ponadto, zaprezentowano model prezentujący relacje pomiędzy tymi cechami a wielkością i gęstością ziaren. Ponieważ ziarna określonej wielkości lub gęstości mogą występować w sąsiednich klasach lub frakcjach, wykonano trzy typy modeli, bazując na analizie regresji. Analiza została wykonana dla trzech typów węgli. Ponieważ modele zawierają różne ilości zmiennych niezależnych do oceny i porównania otrzymanych wyników zastosowano współczynnik determinacji R2, błąd średniokwadratowy (MSE) oraz statystykę Mallowa Cp.
Rocznik
Strony
151--160
Opis fizyczny
Bibliogr. 35 poz., tab., wykr.
Twórcy
  • AGH, University of Science and Technology, Faculty of Mining and Geoengineering, Department of Mineral Processing and Environmental Engineering, Cracow, Poland
  • JSW Innowacje S.A.
  • AGH, University of Science and Technology, Faculty of Mining and Geoengineering, Department of Mineral Processing and Environmental Engineering, Cracow, Poland
Bibliografia
  • 1. BROŻEK M. Evaluation of liberation level of coal mineral fraction on the basis of Hall’s separation curve, Mining Review, 11, pp. 384-387, 1984. (in Polish)
  • 2. COMREY A. L. A first Course in Factor Analysis New York Academic Press, 1973.
  • 3. DOBOSZ M. Statistical analysis of research results, Akademicka Oficyna Wydawnicza Exit Warsaw, 2001. [in Polish]
  • 4. FOSZCZ D., DUCHNOWSKA M., NIEDOBA T., TUMIDAJSKI T. Accuracy of separation parameters resulting errors of chemical analysis, experimental results and data approximation, Physicochemical Problems of Mineral Processing, 52(1), pp. 98-111, 2016.
  • 5. FOSZCZ D., NIEDOBA T., TUMIDAJSKI T. A geometric approach to evaluating the results of Polish copper ores beneficiation, Mineral Resources Management; 34(2), pp. 55–66, 2018.
  • 6. GŁOWIAK S. The reasons for necessity to correct some part of gravitational enrichment theory, Journal of the Polish Mineral Engineering Society, 1(43), pp. 199-210, 2019a.
  • 7. GŁOWIAK S. Assumptions of probabilistic model of grains density distribution in jig bed layers, Journal of the Polish Mineral Engineering Society, 1(43), pp. 211-220, 2019b.
  • 8. JAMRÓZ 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 Switzerland Springer International Publishing, 242, pp. 443-450, 2014a.
  • 9. JAMRÓZ D. Application of multidimensional scaling to classification of various types of coal, Archives of Mining Sciences, 59(2), pp. 413-425, 2014b.
  • 10. JAMRÓZ D. Application of multi-parameter data visualization by means of autoassociative neural networks to evaluate classification possibilities of various coal types, Physicochemical Problems of Mineral Processing, 50(2), pp. 719-734, 2014c.
  • 11. JAMRÓZ 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, 59, pp. 445–450, 2009.
  • 12. JAMRÓZ D., NIEDOBA T. Application of multidimensional data visualization by means of self-organizing Kohonen maps to evaluate classification possibilities of various coal types, Archives of Mining Sciences, 60(1), pp. 39-50, 2015a.
  • 13. JAMRÓZ 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, 50(1), pp. 185-202, 2014.
  • 14. JAMRÓZ D., NIEDOBA T. Comparison of selected methods of multi-parameter data visualization used for classification of coals, Physicochemical Problems of Mineral Processing, 51(2), pp. 769-784, 2015b.
  • 15. JAMRÓZ D., NIEDOBA T., SUROWIAK A., TUMIDAJSKI T. The use of the visualization of multidimensional data using PCA to evaluate possibilities of the division of coal samples space due to their suitability for fluidised gasification, Archives of Mining Sciences, 61(3), pp. 523-535,. 2016.
  • 16. JAMRÓZ D., NIEDOBA T., SUROWIAK A., TUMIDAJSKI T., SZOSTEK R., GAJER M. Application of multi-parameter data visualization by means of multidimensional scaling to evaluate possibility of coal gasification, Archives of Mining Sciences, 62(3), pp. 445-457, 2017.
  • 17. KLINE P. An easy Guide to Factor Analysis Routledge London, 1994.
  • 18. LAWLEY D.N., MAXWELL A.E. Factor Analysis as a Statistical Method London Butterworths, 1971.
  • 19. NIEDOBA T. Application of Relevance Maps in Multidimensional Classification of Coal Types, Archives of Mining Sciences, 60(1), pp. 93-106, 2015.
  • 20. NIEDOBA T. JAMRÓZ D. Visualization of multidimensional data in purpose of qualitative classification of various types of coal, Archives of Mining Sciences, 58(4), pp. 1317-1333, 2013.
  • 21. 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].
  • 22. 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, 4, pp. 235-244, 2009. [in Polish].
  • 23. NIEDOBA T. Multi-parameter data visualization by means of principal component analysis (PCA) in qualitative evaluation of various coal types, Physicochemical Problems of Mineral Processing, 50(2), pp. 575-589, 2014.
  • 24. NIEDOBA T. Statistical analysis of the relationship between particle size and particle density of raw coal, Physicochemical Problems of Mineral Processing, 49(1), pp. 175-188, 2013b.
  • 25. NIEDOBA T. Three-dimensional distribution of grained materials characteristics, in Proceedings of the XIV Balkan Mineral Processing Congress Tuzla Bosnia and Herzegovina, 1, pp. 57-59, 2011.
  • 26. NIEDOBA T., PIĘTA P., SUROWIAK A. Analysis of distributions of various coal types properties by means of statistical methods IOP Conference Series: Materials Science and Engineering art. 012008, 427, pp. 1-5, 2018.
  • 27. 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, 1, pp. 3844-3854, 2012.
  • 28. ÖNEY Ö., The Increase Of The Performance Of Ultrafine Coal Flotation By Using Emulsified Kerosene And The Prediction Of The Flotation Parameters By Random Forest And Genetic Algorithm, Archives of Mining Sciences, 64(1), pp. 119-130, 2019.
  • 29. PIĘTA P., NIEDOBA T., SUROWIAK A., ŞAHBAZ O., KARAGÜZEL C., CANIEREN Ö. Studies on Polish copper ore beneficiation in Jameson cell, IOP Conference Series: Materials Science and Engineering, 427, art. 012009, pp. 1–12, 2018.
  • 30. SOBOLEWSKI A., MICOREK T., WINNICKA G., HEILPERN S. Proposal of Polish Coking Coal Classification, The Polish Mining Review, 72(10), pp. 38-43, 2016. [in Polish]
  • 31. STANISZ A. Easy Course of Statistics, vol. 3, Statsoft Krakow Poland, 2007. [in Polish]
  • 32. STĘPIŃSKI W. Mean values curves, Ores and Non-Ferrous Metals, 9(10), pp. 532-535, 1965. (in Polish)
  • 33. SUROWIAK A. Influence of particle density distributions of their settling velocity for narrow size fractions, Mineral Resources Management, 30, pp. 105-122, 2014. [in Polish]
  • 34. SUROWIAK A. Investigation of hard coal beneficiation destined to gasification process in fluidized bed gas generator, The Polish Mining Review, 69(2), pp. 239–244, 2007. [in Polish]
  • 35. TUMIDAJSKI T., SARAMAK D. Methods and models of mathematical statistics in mineral processing, Wydawnictwo AGH, Krakow, 2009. (in Polish)
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-67ce42f7-a98c-4d4a-9d2c-97658c145477
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