A A A Ц Ц Ц Ц

ШРИФТ:

Arial Times New Roman

ИНТЕРВАЛ:

х1 х1.5 х2

КЕРНИНГ:

Обычный Средний Большой

ИЗОБРАЖЕНИЯ:

Черно-белые Цветные
For the visually impaired Standard Version
Scientific, Technological and Production Journal
ISSN 2079–3332 (print)
ISSN 3034-6835 (online)
founded in 2001

Determination of advance sampling grid parameters for coal quality control in a complex-structure seam

Original articles: 2026 No 5

Determination of advance sampling grid parameters for coal quality control in a complex-structure seam

DOI: 10.56195/20793332-2026-26-5-36-42

Read in pdf E.A. Tagaev
  1. National University of Science and Technology MISIS, Moscow, Russian Federation
p. 36-42, No.5/2026
Abstract:
The paper proposes a methodological approach to determining the parameters of an advance coal sampling grid for a complex-structured seam based on quality indicator modelling. The approach is motivated by the fact that seam-average quality estimates do not reliably predict local intervals that deteriorate the quality of mined coal in seams of complex structure. The methodology was tested for seam H16 of the Northwestern block of the Elga deposit, where the 3rd mineralized pack was treated as an independent geological-technological object. The calculation sample included 115 boreholes, and the Ad100% index accounting for full contamination by intra-seam rock partings was calculated for each borehole. Ad100% ranges from 23.0 to 60.2 %, with an average of 35.0 % and a coefficient of variation of 17.5 %. The procedure included statistical analysis, construction of the experimental variogram, comparison of spherical, Gaussian, exponential and linear models, ordinary kriging, and analysis of the kriging variance map. It was found that the spatial correlation of Ad100% is weak and unstable: the variogram nearly reaches the sill already at the first lag and then fluctuates around it. The kriged contour map of Ad100% and the kriging variance map show that the adequacy of the advance sampling grid should be assessed not only by mean spacing, but also by the grid configuration, local quality heterogeneity and observation density. The proposed approach can be used to compare 14 × 14 m and 20 × 20 m operational grids and to delineate areas where local densification of advance sampling is required.
Keywords:
advance sampling, complex-structured seam, ash content, Ad100%, sieve-float analysis, variogram, kriging, kriging variance, sampling grid
For citation:
Tagaev E.A. Determination of advance sampling grid parameters for coal quality control in a complex-structure seam. Mine Surveying and Subsurface Use. 2026; 26 (5): 36-42. (In Russ.). https://doi.org/10.56195/20793332-2026-26-5-36-42
Information about the authors:
  1. Egor A. Tagaev – postgraduate student, National University of Science and Technology MISIS, Moscow, Russian Federation; https://orcid. org/0009-0009-0477-8841; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
References:
  1. 1. Кузнецов П.Ю., Гриб Н.Н., Качаев А.В. Анализ репрезентативности данных о качестве угля для создания базы данных по результатам детальной геологической разведки. Международный научно-исследовательский журнал. 2016; 6-5 (48): 137-140. https://doi.org/10.18454/ IRJ.2016.48.081. Kuznetsov P.Yu., Grib N.N., Kachaev A.V. Analysis of representativeness of coal quality data for creation of a database based on detailed geological exploration. International Research Journal. 2016; 6-5 (48): 137-140. (In Russ.). https://doi.org/10.18454/IRJ.2016.48.081.
  2. 2. Батугин С.А., Гаврилов В.Л., Хоютанов Е.А. Оценка влияния тонких породных прослоев на зольность угля Эльгинского месторождения. Науковедение. 2015; 7 (4). https://doi.org/10.15862/136TVN415. Batugin S.A., Gavrilov V.L., Khoyutanov E.A. Assessment of the influence of thin rock partings on ash content of coal at the Elga deposit. Naukovedenie. 2015; 7 (4). (In Russ.). https://doi.org/10.15862/136TVN415.
  3. 3. Хоютанов Е.А., Гаврилов В.Л. Метод оценки природной и технологической составляющих зольности добываемого угля. Физико- технические проблемы разработки полезных ископаемых. 2018; 5: 88-100. https://doi.org/10.15372/FTPRPI20180509. Khoyutanov E.A., Gavrilov V.L. Method for estimating natural and technological components of ash content in mined coal. Journal of Mining Science. 2018; 5: 88-100. (In Russ.). https://doi.org/10.15372/FTPRPI20180509.
  4. 4. Хоютанов Е.А., Гаврилов В.Л. Управление качеством добываемого угля при разработке сложноструктурных месторождений. Физико- технические проблемы разработки полезных ископаемых. 2019; 3: 62-71. https://doi.org/10.15372/FTPRPI20190308. Khoyutanov E.A., Gavrilov V.L. Quality control of mined coal in development of complex-structured deposits. Journal of Mining Science. 2019; 3: 62-71. (In Russ.). https://doi.org/10.15372/FTPRPI20190308.
  5. 5. Kuznetsov P.Yu., Grib N.N., Zhelyabina A.G. Exploration Testing Network Optimization at the Operational Stage of Coal Mining. IOP Conference. Series: Earth and Environmental Science. 2019; 272: 022034. https://doi.org/10.1088/1755-1315/272/2/022034.
  6. 6. Fisonga M., Wang F., Mutambo V. The estimation of sampling density in improving geostatistical prediction for geotechnical characterization. International Journal of Geotechnical Engineering. 2021; 15 (6): 724-731. https://doi.org/10.1080/19386362.2018.1526484.
  7. 7. Hernandez Guerra H., Alberdi E., Goti A. Influence of the sampling density in the coestimation error of a regionalized locally stationary variable. Minerals. 2020; 10 (2): 90. https://doi.org/10.3390/min10020090.
  8. 8. Amri N.A., Wulandari S., Cahyadi T.A., et al. Distance of Drill-Hole Determination based on Global Estimation Variance of Coal Resources Classification. Journal of Advanced Research in Applied Sciences and Engineering Technology. 2023; 33 (1): 267-274. https://doi.org/10.37934/ araset.33.1.267274.
  9. 9. Purba M.J., Wicaksono E. Optimization of Information Point Distances in the Estimation and Classification of Coal Resources Using Geostatistical Methods Compared to SNI 5015:2019 for Moderate Geological Conditions. Mining Technology Journal. 2023; 1 (1). https://doi.org/10.31315/mtj. v1i1.6826.
  10. 10. Ertunç G., Tercan A.E., Hindistan M.A, et al. Geostatistical estimation of coal quality variables by using covariance matching constrained kriging. International Journal of Coal Geology. 2013; 112: 14-25. https://doi.org/10.1016/j.coal.2012.11.014.
  11. 11. Pardo-Igúzquiza E., Dowd P.A., Baltuille J.M., et al. Geostatistical modelling of a coal seam for resource risk assessment. International Journal of Coal Geology. 2013; 112: 134-140. https://doi.org/10.1016/j.coal.2012.11.004.
  12. 12. Jeuken R., Xu C., Dowd P. Improving Coal Quality Estimations with Geostatistics and Geophysical Logs Natural Resources Research. 2020; 29 (4): 2529-2546. https://doi.org/10.1007/s11053-019-09609-y.
  13. 13. Sideri D., Roumpos C., Pavloudakis F., et al. Multivariate Geostatistical Modeling of Lower Calorific Value in Multi-Seam Coal Deposits. Applied Sciences. 2020; 10 (18): 6208. https://doi.org/10.3390/app10186208.
  14. 14. Gül Y., Küçükkarasu O. Resource estimation for Alpagut-Dodurga coal field and determination of spatial distribution of coal quality parameters. Turkish Journal of Earth Sciences. 2020; 29 (3): 521-537. https://doi.org/10.3906/yer-1910-17.
  15. 15. Faidatulaila R., Marwanza I., Purwiyono T. T. Variography analysis on the assessment of coal deposit quality using the ordinary kriging method. AIP Conference Proceedings. 2023; 2598: 060002. https://doi.org/10.1063/5.0126896.
  16. 16. Копытов А.И., Рогова Т.Б., Шаклеин С.В. Оценка ожидаемых погрешностей геометризации угольных месторождений на стадиях разведки и эксплуатации. Горный журнал. 2020; 4: 12-16. https://doi.org/10.17580/gzh.2020.04.01. Kopytov A.I., Rogova T.B., Shaklein S.V. Estimation of expected errors in geometrization of coal deposits at the stages of exploration and mining. Mining Journal. 2020; 4: 12-16. (In Russ.). https://doi.org/10.17580/gzh.2020.04.01.
  17. 17. Кантемиров В.Д., Яковлев А.М., Титов Р.С. Оценка качественных показателей минерального сырья с использованием геоинформационных технологий блочного моделирования. Геоинформатика. 2020; 3: 29-37. https://doi.org/10.47148/1609-364X-2020-3-29-37. Kantemirov V.D., Yakovlev A.M., Titov R.S. Assessment of quality indicators of mineral resources using geoinformation technologies of block modeling. Geoinformatika. 2020; 3: 29-37. (In Russ.). https://doi.org/10.47148/1609-364X-2020-3-29-37.
  18. 18. Сидорова Г.П., Маниковский П.М. Прогнозирование качества угля с использованием блочной модели пласта на примере Кутинского буроугольного месторождения. Горный информационно-аналитический бюллетень. 2022; 12: 55-66. https://doi.org/10.25018/0236_1493_2022_12_0_55. Sidorova G.P., Manikovskii P.M. Forecasting coal quality using a seam block model: a case study of the Kutinsky brown coal deposit. Mining information and analytical bulletin. 2022; 12: 55-66. (In Russ.). https://doi.org/10.25018/0236_1493_2022_12_0_55.
  19. 19. Noble A., Luttrell G.H. A review of state-of-the-art processing operations in coal preparation. International Journal of Mining Science and Technology. 2015; 25 (4): 511-521. https://doi.org/10.1016/j.ijmst.2015.05.001.
  20. 20. Stone D., Campbell Q.P., le Roux M., et al. Assessment of coal washability data obtained via the RhoVol analyser. Journal of the Southern African Institute of Mining and Metallurgy. 2023; 123 (10): 509-512. https://doi.org/10.17159/2411-9717/1047/2023.
  21. 21. Sriramoju S.K., Singh R., Sengupta M., et al. Selective screening of coal to improve the washability characteristics at different levels of size reduction. International Journal of Coal Preparation and Utilization. 2021. https://doi.org/10.1080/19392699.2021.1932845.
  22. 22. Maxwell K., Rajabi M., Esterle J., et al. Spatial modelling and classification of altered coal using random forest-based methods at Moatize Basin, Mozambique. Journal of African Earth Sciences. 2024; 105279. https://doi.org/10.1016/j.jafrearsci.2024.105279.

 

Горнодобывающая промышленность: инвестиционные проекты и меры поддержки

New issue

The new issue of the journal «Mine surveying and Subsurface Use» has been published
No. 5 (vol.26), September-October 2026

New materials