Mapping of Compositional Properties of Coal Using Isometric Log-Ratio Transformation and Sequential Gaussian Simulation – a Comparative Study for Spatial Ultimate Analyses Data

Details

• Personal Author:
  Karacan, C. Özgen ; Olea RA
• Description:
  Chemical properties of coal largely determine coal handling, processing, beneficiation methods, and design of coal-fired power plants. Furthermore, these properties impact coal strength, coal blending during mining, as well as coal's gas content, which is important for mining safety. In order for these processes and quantitative predictions to be successful, safer, and economically feasible, it is important to determine and map chemical properties of coals accurately in order to infer these properties prior to mining. Ultimate analysis quantifies principal chemical elements in coal. These elements are C, H, N, S, O, and, depending on the basis, ash, and/or moisture. The basis for the data is determined by the condition of the sample at the time of analysis, with an "as-received" basis being the closest to sampling conditions and thus to the in-situ conditions of the coal. The parts determined or calculated as the result of ultimate analyses are compositions, reported in weight percent, and pose the challenges of statistical analyses of compositional data. The treatment of parts using proper compositional methods may be even more important in mapping them, as most mapping methods carry uncertainty due to partial sampling as well. In this work, we map the ultimate analyses parts of the Springfield coal from an Indiana section of the Illinois basin, USA, using sequential Gaussian simulation of isometric log-ratio transformed compositions. We compare the results with those of direct simulations of compositional parts. We also compare the implications of these approaches in calculating other properties using correlations to identify the differences and consequences. Although the study here is for coal, the methods described in the paper are applicable to any situation involving compositional data and its mapping. [Description provided by NIOSH]
• Subjects:
  Chemistry Coal Coal Mining Mining Occupational Health Safety
• Keywords:
  Author Keywords: Calorific Value Chemical Analysis Chemical Composition Chemical Properties Coal Gas Coal Mining Coal Processing Coal Quality Compositional Modeling Data Processing Elements Fossil Fuels Mine Safety Quantitative Analysis Regression Sampling Simulation Methods Springfield Coal Statistical Analysis

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• Series:
  Mining Publications
• ISSN:
  0375-6742
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; Pennsylvania ; Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  PMRD - Pittsburgh Mining Research Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  36-49
• Volume:
  186
• NIOSHTIC Number:
  nn:20050794
• Citation:
  J Geochem Explor 2018 Mar; 186:36-49
• Contact Point Address:
  C. Özgen Karacan, USGS Eastern Energy Resources Science Center, Reston, VA, USA
• Email:
  ckaracan@usgs.gov
• CAS Registry Number:
  Carbon (CAS RN 7440-44-0) ; Hydrogen (CAS RN 1333-74-0) ; Nitrogen (CAS RN 7727-37-9) ; Oxygen (CAS RN 7782-44-7) ; Sulfur (CAS RN 7704-34-9)
• Federal Fiscal Year:
  2018
• Peer Reviewed:
  True
• Source Full Name:
  Journal of Geochemical Exploration
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0ec2a605719c3567ba12f2c701d162c567b369796c4fc0839e83710343309ab431094cbc86f90083c0124115bafe3b995c8112e45b7075402b1f6e07e76c04b7
• Download URL:
  https://stacks.cdc.gov/view/cdc/227616/cdc_227616_DS1.pdf
• File Type:
  [PDF - 3.99 MB ]