In-situ coal quality variability analysis by combining Gaussian co-simulation and a JavaScript

References

• Abichequer, L. A., J. F. C. L. Costa, H. A. Pasti, and J. C. Koppe. 2011. Design of blending piles by geostatistically simulated models - A real case reconciliation. International Journal of Mineral Processing 99 (1–4):21–26. doi:10.1016/j.minpro.2011.02.002.
   Google Scholar
• Badani-Prado, M. A., V. Kecojevic, and D. Bogunovic. 2016. Coal quality management model for dome storage (DS-CQMM). Journal of the Southern African Institute of Mining and Metallurgy 116 (7):699–708. doi:10.17159/2411-9717/2016/v116n7a12.
   Web of Science ®Google Scholar
• Barca, E., E. Porcu, D. Bruno, and G. Passarella. 2017. An automated decision support system for aided assessment of variogram models. Environmental Modelling and Software 87:72–83. Elsevier Ltd. doi:10.1016/j.envsoft.2016.11.004.
   Web of Science ®Google Scholar
• Benndorf, J. 2013. Investigating in situ variability and homogenisation of key quality parameters in continuous mining operations. Mining Technology 122 (2):78–85. doi:10.1179/1743286312Y.0000000027.
   Google Scholar
• Benndorf, J. 2013. Application of efficient methods of conditional simulation for optimising coal blending strategies in large continuous open pit mining operations. International Journal of Coal Geology 112:141–53. doi:10.1016/j.coal.2012.10.008.
   Web of Science ®Google Scholar
• Beretta, F. S., J. F. C. L. Costa, and J. C. Koppe. 2010. Reducing coal quality attributes variability using properly designed blending piles helped by geostatistical simulation. International Journal of Coal Geology 84(2):83–93. Elsevier B.V. doi:10.1016/j.coal.2010.08.007.
   Web of Science ®Google Scholar
• Ersoy, A., and T. Y. Yünsel. 2018. The assessment of soil contamination by heavy metals using geostatistical sequential Gaussian simulation method. Human and Ecological Risk Assessment 24(8):2142–61. Taylor & Francis. doi:10.1080/10807039.2018.1440528.
   Web of Science ®Google Scholar
• Ersoy, A., and T. Y. Yünsel. 2008. Assessment of lignite quality variables: A practical approach with sequential Gaussian simulation. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 31 (2):175–90. doi:10.1080/15567030701522260.
   Web of Science ®Google Scholar
• Faulkner, B. R. 2002. Java classes for nonprocedural variogram modeling. Computers and Geosciences 28 (3):387–97. doi:10.1016/S0098-3004(01)00049-8.
   Google Scholar
• Geovariances. 2016. “ISATIS 2016 beginner’s guide,” 240.
   Google Scholar
• Heriawan, M. N., and K. Koike. 2008a. Identifying spatial heterogeneity of coal resource quality in a multilayer coal deposit by multivariate geostatistics. International Journal of Coal Geology 73 (3–4):307–30. doi:10.1016/j.coal.2007.07.005.
   Web of Science ®Google Scholar
• Heriawan, M. N., and K. Koike. 2008b. Uncertainty assessment of coal tonnage by spatial modeling of seam distribution and coal quality. International Journal of Coal Geology 76 (3):217–26. doi:10.1016/j.coal.2008.07.014.
   Web of Science ®Google Scholar
• Hohn, M., Ed. December 2015. Collocated cosimulation of permeabilty in an appalachian oil field. 2003:1–8. doi:10.13140/RG.2.1.1426.3128.
   Google Scholar
• Huang, J. H., W. C. Liu, G. M. Zeng, F. Li, X. L. Huang, Y. L. Gu, L. X. Shi, Y. H. Shi, and J. Wan. 2016. An exploration of spatial human health risk assessment of soil toxic metals under different land uses using sequential indicator simulation. Ecotoxicology and Environmental Safety 129:199–209. Elsevier. doi:10.1016/j.ecoenv.2016.03.029.
   PubMed Web of Science ®Google Scholar
• Kim, K. S., B. H. Yoo, V. Shelia, C. H. Porter, and G. Hoogenboom. 2018. START: A data preparation tool for crop simulation models using web-based soil databases. Computers and Electronics in Agriculture 154:256–64. November 2017. Elsevier. doi:10.1016/j.compag.2018.08.023.
   Web of Science ®Google Scholar
• Maleki, M., and X. Emery. March 2017. Joint simulation of stationary grade and non-stationary rock type for quantifying geological uncertainty in a copper deposit. Computers and Geosciences 109:258–67. Elsevier Ltd. doi:10.1016/j.cageo.2017.08.015.
   Google Scholar
• Molayemat, H., F. M. Torab, V. Pawlowsky-Glahn, A. H. Morshedy, and J. J. Egozcue. 2018. The impact of the compositional nature of data on coal reserve evaluation, a case study in Parvadeh IV coal deposit, Central Iran. International Journal of Coal Geology 188:94–111. December 2017. Elsevier. doi:10.1016/j.coal.2018.02.003.
   Web of Science ®Google Scholar
• Mondal, A., S. Das, R. K. Sah, P. Bhattacharyya, and S. S. Bhattacharya. 2017. Environmental footprints of brick kiln bottom ashes: Geostatistical approach for assessment of metal toxicity. Science of the Total Environment 609:215–24. Elsevier B.V. doi:10.1016/j.scitotenv.2017.07.172.
   PubMed Web of Science ®Google Scholar
• Naworyta, W., S. Sypniowski, and J. Benndorf. 2015. Planning for reliable coal quality delivery considering geological variability: A case study in polish lignite mining. Journal of Quality and Reliability Engineering 2015:1–9. doi:10.1155/2015/941879.
   Google Scholar
• Olea, R. A., J. A. Luppens, J. J. Egozcue, and V. Pawlowsky-Glahn. 2016. Calorific value and compositional ultimate analysis with a case study of a Texas lignite. International Journal of Coal Geology 162:27–33. Elsevier B.V. doi:10.1016/j.coal.2016.05.005.
   Web of Science ®Google Scholar
• Olea, R. A., J. A. Luppens, and S. J. Tewalt. 2011. Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit. International Journal of Coal Geology 85(1):78–90. Elsevier B.V. doi:10.1016/j.coal.2010.10.001.
   Web of Science ®Google Scholar
• Pardo-Igúzquiza, E., P. A. Dowd, J. M. Baltuille, and M. Chica-Olmo. 2013. Geostatistical modelling of a coal seam for resource risk assessment. International Journal of Coal Geology 112:134–40. doi:10.1016/j.coal.2012.11.004.
   Web of Science ®Google Scholar
• Regli, C., L. Rosenthaler, and P. Huggenberger. 2004. GEOSSAV: A simulation tool for subsurface applications. Computers and Geosciences 30 (3):221–38. doi:10.1016/j.cageo.2003.10.007.
   Google Scholar
• Rivas, T., J. M. Matías, J. Taboada, and A. Argüelles. 2007. Application of bayesian networks to the evaluation of roofing slate quality. Engineering Geology 94 (1–2):27–37. doi:10.1016/j.enggeo.2007.06.002.
   Web of Science ®Google Scholar
• Rühaak, W. 2006. A Java application for quality weighted 3-d interpolation. Computers & Geosciences 32(1):43–51. Tarrytown, NY, USA: Pergamon Press, Inc. doi:10.1016/j.cageo.2005.04.005.
   Web of Science ®Google Scholar
• Shen, G., and M. Zhang. 2016. Multi-scale regional forest carbon density estimation based on regression and sequential Gaussian co-simulation. Acta Ecological Sinica 36(2):62–71. Elsevier B.V. doi:10.1016/j.chnaes.2016.01.002.
   Google Scholar
• Shih, J. S., and H. Christopher Frey. 1995. Coal blending optimization under uncertainty. European Journal of Operational Research 83 (3):452–65. doi:10.1016/0377-2217(94)00243-6.
   Web of Science ®Google Scholar
• Wackernagel, H. 1996. Multivariate geostatistics – An introduction with applications. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts 33:363A–363A. doi:10.1016/S0148-9062(97)87459-1.
   Google Scholar
• Yu, J., L. Weidong, and C. Zhang. 2018. A framework of experimental transiogram modelling for markov chain geostatistical simulation of landscape categories. Computers, Environment and Urban Systems 1–11. no. July. Elsevier. doi:10.1016/j.compenvurbsys.2018.07.007.
   Web of Science ®Google Scholar