APPLICATION OF STOCHASTIC MODELS FOR MINE PLANNING AND COAL QUALITY CONTROL

Abstract

The power plant owner is interested to know in advance the quality of coal to be burnt which should meet maximal efficiency of power plant and the environmental regulations. There is the need to control and to predict the quality of coal at the mine site to meet sulfur emission requirements. Coal quality control between the mine site and the utility plant is a complex problem owing to the variable nature of coal properties (heating value, sulfur, ash), even within the same coal seam. Due to the fluctuation of the coal quality, mine planning and coal homogenization are in fact an optimization problem under uncertain conditions. To deal with these issues a stochastic optimization model is developed for an illustrative coal homogenization problem. Mining block grades from an optimized mining schedule are used to simulate any given homogenization process in stockpiles throughout the mine`s life. Sulfur content is treated as lognormally distributed random variable. The objectives of the model include minimizing the expected sulfur content and standard deviation in sulfur content. The methodology was illustrated using the case study on Kolubara surface coal mine.

Keywords

Dates

  • Submission Date2013-02-01
  • Revision Date2013-02-10
  • Acceptance Date2013-04-04
  • Online Date2013-04-13

DOI Reference

10.2298/TSCI130201031S

References

  1. ***, International energy outlook 2011, US Energy Information administration, 2011.
  2. Ignjatović D., Knežević D., Kolonja B., Lilić N., Stanković R.: Upravljanje kvalitetom uglja ("Coal quality management"), University of Belgrade, Faculty of mining and geology, Belgrade, 2007.
  3. Shih, J.S. and Frey, H.C. Coal blending optimization under uncertainty. Proceedings of the Tenth Annual International Pittsburgh Coal Conference, Pittsburgh, Pennsylvania, 1993, pp. 1110-1115.
  4. David, M., Developments in Geomathematics 2: Geostatistical ore reserve estimation, First Edition.: Elsevier , Amsterdam 1977.
  5. Parker, H. The Volume Variance Relationship: A Useful Tool for Mine Planning. Engineering and Mining Journal, vol. 180 (1979), pp.106-123.
  6. Schofield, C.G., Homogenisation/Blending Systems Design and Control for Minerals Processing, TransTech Publications, Claustehall 1980.
  7. ***, A review of the state-of-the-art in coal blending for power generation, Final Report - Project 3.16., CRC, 2001,
  8. Isaaks, E.H. and Srivastava, R.M., Applied Geostatistics, Oxford University Press, New York 1989.
  9. Costa, J.C. L., Marques, D., Pilger, G., Koppe, J. C., Ribeiro, D.T., Batiston, E.L. and Costa, M.A., Incorporating in situ grade variability into blending piles design using geostistical simulation, Proceedings of the 3rd World Conference on Sampling and Blending, Porto Alegre. vol. 1, 2007. pp. 378-390.
  10. Gershon. M. E., Mine Scheduling Optimization with Mixed Integer Programming, AlME Fall Meeting, Honolulu, Preprint No. 82- 324, 1982.
  11. Kim, Y. C., Knudsen, H. P. and Baafi, E, Y., Development of Emission Control Strategies Using Conditionally Simulated Coal Deposits, University of Arizona, Tucson, Proprietary report prepared for the Homer City Owners, Homer City, Pa. 15478, 1981.
  12. Padberg, M W, Linear optimization and extensions, Springer, NY, 1995.
  13. De Wet N., Homogenizing/Blending in South Africa - An update, Bulk solids handling, Issue no.1, 1994.
  14. Bivand, R., Pebesma, E., Rubio, V., Applied Spatial Data Analysis with R. Use R Series, Springer, Heidelberg, 2008.
  15. Murphy, T.D., Brown, K.E., Combining geostatistics and simulation to predict sulphur at a central Illinois coal mine, Mining Engineering (1993), 45, 284-287.
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