Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms
| Autor: | Zoran Gligorić, Slavko Torbica, Čedomir Beljić, Jasmina Nedeljkovic Ostojic, Miloš Gligorić, Svetlana Štrbac Savić |
|---|---|
| Přispěvatelé: | Enrico Sciubba |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Engineering Control and Optimization technological model Rand index Energy Engineering and Power Technology Thermal power station 02 engineering and technology computer.software_genre Fuzzy logic lcsh:Technology 020501 mining & metallurgy fuzzy TOPSIS 0202 electrical engineering electronic engineering information engineering Coal Electrical and Electronic Engineering Cluster analysis Engineering (miscellaneous) fuzzy C-mean clustering adjusted Rand index Renewable Energy Sustainability and the Environment business.industry lcsh:T block model TOPSIS Fukuyama-Sugeno validity functional coal deposit Production planning 0205 materials engineering 020201 artificial intelligence & image processing Data mining business entropy Block size computer Energy (miscellaneous) |
| Zdroj: | Energies, Vol 9, Iss 12, p 1059 (2016) Energies Energies; Volume 9; Issue 12; Pages: 1059 |
| ISSN: | 1996-1073 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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