Towards consistent interpretations of coal geochemistry data on whole-coal versus ash bases through machine learning

Na Xu, Mengmeng Peng, Qing Li, Chuanpeng Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

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 logratio 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.

Original languageEnglish
Article number328
Pages (from-to)1-19
Number of pages19
JournalMinerals
Volume10
Issue number4
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Ash basis
  • Correlation
  • Hierarchical clustering algorithm
  • Prediction
  • Whole-coal basis

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geology

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