Application of efficient methods of conditional simulation for optimising coal blending strategies in large continuous open pit mining operations

The delivery of in-spec coal qualities is essential for an efficient and environmental friendly operation of modern coal-fired power plants. The design of the mining operation systems and blending opportunities plays a key role in homogenising variability and improving the prediction of key quality parameters, such as the calorific value (CV).Modern methods of conditional simulation in geostatistics allow for generating several realisations for large deposits capturing in-situ variability of key quality parameters. Integrating simulated realisations of the deposit with a simulation of transport- and blending models of mining operation leads to valuable insights into its performance as a function of the technical design and operational mode.The contribution first reviews the method of Generalised Sequential Gaussian Simulation (GSGS), which is especially designed for computational efficient simulation of large deposits. In a second step GSGS is applied to a large coal field in Eastern Europe. The practical simulation process is described and applied in a complex geological environment of highly variable seam geometry and quality including multiple split seams. Results are applied to a large open pit coal operation to investigate the variability of the calorific value and its behaviour along the extraction, transportation and blending process in a continuous mining environment.The described approach provides a valuable view into the performance of a continuous mining system in terms of homogenisation. Conclusions can be drawn to optimise the design of key equipment and to adjust the operation mode to ensure that the customer's requirements in terms of coal quality variability are met with high probability.

► Integration of simulated deposit models with mining system simulation.
► Linking exploration, in-situ variability and mine system design.
► Large scale 3D application of geostatistical simulation in lignite.