Towards Consistent Interpretations of Coal Geochemistry Data on Whole-Coal versus Ash Bases through Machine Learning.

Coal geochemistry compositional data on whole-coal basis can be converted back to ash basis based on samples' loss on ignition. However, the correlation between the concentrations of elements reported on whole-coal versus ash bases in many cases is inconsistent. Traditional statistical methods (e.g., correlation analysis) for compositional data on both bases may sometimes result in misleading results. To address this issue, we hereby propose an improved additive log-ratio data transformation method for analyzing the correlation between element concentrations reported on whole-coal versus ash bases. To verify the validity of the method proposed in this study, a data set which contains comprehensive analyses of 106 Late Paleozoic coal samples from the Datanhao mine and Adaohai Mine, Inner Mongolia, China, is used for the validity testing. A prediction model was built for performance evaluation of two methods based on the hierarchical clustering algorithm. The results show that the improved additive log-ratio is more effective in prediction for occurrence modes of elements in coal than the previously reported stability method, and therefore can be adopted for consistent interpretations of coal geochemistry compositional data on whole-coal vs. ash bases. [ABSTRACT FROM AUTHOR]