Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms

Miloš Gligorić, Zoran Gligorić, Čedomir Beljić, Slavko Torbica, Svetlana Štrbac Savić and Jasmina Nedeljković Ostojić
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Miloš Gligorić: Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia
Zoran Gligorić: Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia
Čedomir Beljić: Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia
Slavko Torbica: Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia
Svetlana Štrbac Savić: The School of Electrical and Computer Engineering of Applied Studies, Vojvode Stepe 283, 11000 Belgrade, Serbia
Jasmina Nedeljković Ostojić: Department of Geodesy, Belgrade University College of Applied Studies in Civil Engineering and Geodesy, Hajduk Stanka 2, 11000 Belgrade, Serbia

Energies, 2016, vol. 9, issue 12, 1-23

Abstract: The main aim of a coal deposit model is to provide an effective basis for mine production planning. The most applied approach is related to block modeling as a reasonable global representation of the coal deposit. By selection of adequate block size, deposits can be well represented. A block has a location in XYZ space and is characterized by adequate attributes obtained from drill holes data. From a technological point of view, i.e., a thermal power plant’s requirements, heating value, sulfur and ash content are the most important attributes of coal. Distribution of attributes’ values within a coal deposit can vary significantly over space and within each block as well. To decrease the uncertainty of attributes’ values within blocks the concept of fuzzy triangular numbers is applied. Production planning in such an environment is a very hard task, especially in the presence of requirements. Such requirements are considered as target values while the values of block attributes are the actual values. To make production planning easier we have developed a coal deposit model based on clustering the relative closeness of actual values to the target values. The relative closeness is obtained by the TOPSIS method while technological clusters are formed by fuzzy C-mean clustering. Coal deposits are thus represented by multi-attribute technological mining cuts.

Keywords: coal deposit; block model; technological model; fuzzy TOPSIS; fuzzy C-mean clustering; Fukuyama-Sugeno validity functional; adjusted Rand index; entropy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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