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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Miloš Gligorić & Zoran Gligorić & Čedomir Beljić & Slavko Torbica & Svetlana Štrbac Savić & Jasmina Nedeljković Ostojić, 2016. "Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms," Energies, MDPI, vol. 9(12), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1059-:d:85273
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    References listed on IDEAS

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    1. Lamghari, Amina & Dimitrakopoulos, Roussos, 2016. "Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty," European Journal of Operational Research, Elsevier, vol. 250(1), pages 273-290.
    2. Meila, Marina, 2007. "Comparing clusterings--an information based distance," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 873-895, May.
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    Cited by:

    1. Weixin Yang & Lingguang Li, 2017. "Efficiency Evaluation and Policy Analysis of Industrial Wastewater Control in China," Energies, MDPI, vol. 10(8), pages 1-18, August.

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