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.

Improving efficiency of thermal power plants through mine coal quality planning and control

“…Successful production scheduling of coal extraction process is based on a reliable estimate of the spatial distribution of coal quality in the deposit and aims to define an extraction sequence that meets daily production targets in terms of coal tonnage produced and coal quality parameters (calorific value, sulphur content, or ash content) [7,8]. The unit of daily production targets is usually a train load in the sequence of approximately 1500 tones that is shipped to the thermal power plant.…”

“…Optimization of division within a region is carried out as follows: (6) finish if the height of the region does not exceed two minimal sub-bench height; (7) the minimal number of sub-benches, M, is ratio of the total height of the region and sub-benches maximum height, considering the first and the last sub-bench constraints; (8) the maximum number of subbenches, N, is total height of the region divided by sub-benches minimal height; (9) for k from M to N initiate a genetic algorithm attempting to divide the region to sub-benches until the right solution is found.…”

“…Calculation of the intersection of the layers and slices provides the volume contribution of the layers in the slices v i and v j in eq. (7). The homogenization efficiency is then obtained by calculating the statistical properties of the output (standard deviation and auto correlation function) and comparing them with the statistical properties of the input.…”

Hybrid model for uncertainty assessment in open pit optimization

“…For dealing with various mining and geology problems, stochastic approach is suggested by many authors [6][7][8][9]. This paper presents a stochastic model for calculating the EBV.…”