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.
Highlights
Thermal power plants around the world primarily rely on coal produced by surface and underground mines
We focus on the coal mine, i.e., on the coal deposit representing the source of feedstock for the thermal power plant
Based on Equations (21)–(26) we describe the model to coal deposit partitioning into technological mining cuts as follows: Step 1: select an integer number of technological mining cuts i.e., clusters (N) and threshold value ε; let ω = 2; Step 2: input a set of initial cluster centers [c1, c2, . . . , cn ], composed of the increasing order values randomly chosen from the interval [min{si }, max {si }], i = 1, 2.., k; q
Summary
Thermal power plants around the world primarily rely on coal produced by surface and underground mines. The technological model significantly reduces the number of constraints i.e., only one constraint is employed This means that if the relative closeness of the mining cut is greater than a predefined value, all blocks belonging to the cut will be processed, otherwise they will be treated as waste. Calculation of the relative closeness can be treated as an Alternatives, Criteria, Evaluations model; where alternatives are mineable blocks, criteria are distances between block attributes and required values and evaluations represent the rating of the alternative with respect to defined criterion. Selection of the optimal way of clustering is based on the comparison of the obtained adjusted Rand indexes, entropies and Fukuyama-Sugeno validity functionals
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