Abstract
The estimation of fuel characteristics and spatial variability in multi-seam coal deposits is of great significance for the optimal mine planning and exploitation, as well as for the optimization of the corresponding power plants operation. It is mainly based on the quality properties of the coal (i.e., Lower Calorific Value (LCV), ash content, CO2, and moisture). Even though critical, these properties are not always measured in practice for all available borehole samples, or, they are generally estimated by using non-parametric statistics. Therefore, spatial modeling of LCV can become problematic due to the limited number of data. Thus, the use of other available correlated attributes might be helpful. In this research, techniques of multivariate geostatistics were used to estimate and evaluate the spatial distribution of quality properties in a multi-seam coal deposit, with special reference to the LCV. More specifically, kriging, cokriging, and Principal Component Analysis (PCA) techniques were tested in a case study as estimators of the LCV, using an extensive set of borehole data from the South Field lignite mine in Ptolemais, Greece. The research outcomes show that the application of kriging with two PCA factors and the use of inverse transform result in the best LCV estimates. Moreover, cokriging with two auxiliary variables gives more accurate values for a LCV estimate, in relation to the kriging technique. The research outcomes could be considered significant for the coal mining industry, since the use of correlated quality attributes for the estimation of LCV may contribute to a reduction of the estimation uncertainty at no additional drilling cost.
Highlights
Greek lignite deposits have a multiple-layered structure consisting of several coal seams which are separated mainly by calcareous and argillaceous waste beds (Figure 1)
The aim of the geostatistical analysis performed in this research is to determine the best estimation conditions of the Lower Calorific Value, taking into account all the available information concerning the quality parameters of coal, provided in the data set
The utilization of proper methodologies to ease the inclusion of these auxiliary variables in the estimation procedure. Among such methodologies, kriging and cokriging are geostatistical tools that exploit the spatial structure in order to map the objective variable on a given domain [18,23]; kriging by taking into account measurements of the objective variable in the neighborhood of the estimation points, while cokriging by considering auxiliary variables [23,24]
Summary
Greek lignite deposits have a multiple-layered structure consisting of several coal seams which are separated mainly by calcareous and argillaceous waste beds (Figure 1). The necessity of selective mining due of the above stratigraphic deposit structure, combined with the requirements for high production rates, was the reason for the application of the continuous surface mining method for more than sixty years [1]. The modeling and evaluation of multi-seam coal deposits and, the estimation of the mineable coal reserves and their quality properties is a process of compositing seams into blocks of exploitable coal seams and separating them from the blocks of waste (non-lignite) material [2].
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