Evaluation of elemental affinities in coal using agglomerative hierarchical clustering algorithm: A case study in a thick and mineable coal seam (kM2) from Soma Basin (W. Turkey)

Abstract

The hierarchical clustering algorithm, especially Pearson correlation coefficient, along with other statistical approach, is used statistical approach for determining the toxic elements affinities in coal, and this method is one of the common approaches due to the correlations between some elements with minerals that cannot be chemically affiliated. This study aims to correlate geochemical and mineralogical and data of the lower seam (up to 30 m) in the Soma coalfield with Cosine and Bray-Curtis similarity measures, Chebyshev and Canberra distance metrics, and also Pearson correlation and Tanimoto coefficients. The results have been evaluated using agglomerative hierarchical clustering algorithm with average linkage, and elements are grouped in several clusters. Some of the similarity measures and distance metrics do not seem to agree with mineralogical and geochemical data. However, elements affiliated with aluminosilicate elements (e.g., Al, K, and Cs) are grouped, and elements (e.g., As, Mo and U) related to redox conditions in coal depositional environment are located in the same group according to Pearson correlation coefficient and Cosine similarity. In addition, this data has been observed in agreement with SEM-EDX and XRD data of studied coal samples. The present study indicates that Cosine similarity could be an alternative for the Pearson correlation coefficient in coal studies.