Grade control is crucial for the optimization of mineral resource extraction in open pit mining. This process involves sampling, estimation, and classification of mined material for determining economic ore and wasteful material. There are two very important issues in such activities, namely ore misclassification and dilution. Ore misclassification is defined as the situation where waste is classified as ore and results in the dilution of mill feed or vice versa, economic ore is classified as waste and lost in the process. Dilution means the inevitable inclusion of sub-economic material in the block of mined material that consequently leads to the decrease in its feed grade.
The basis of the whole grade control system is blast hole drilling. For bulk open pit mining the holes are drilled densely to fracture the rock and the drill cuttings are collected for further sampling. The main goal is the estimation of bench grade prior to excavation. Such data helps mining engineers to determine the boundaries between ore and waste using empirical data.
The design of this drilling program demands careful consideration of sampling protocols and resolution. The distance between drill holes should be based on the degree of geological heterogeneity of the particular mineral deposit. The overly broad grid will cause oversmoothing of high-grade areas due to the interpolation of spatial data and will amplify the effect of volume-variance, which is the difference between the sample size and the SMU size. Quality unbiased sampling at reasonable intervals is crucial in minimizing uncertainties of block model estimations.
In recent times, modern grade control programs involve the use of innovative and data-driven technologies for improving the level of accuracy. An example of such a technology would be the use of hyperspectral imaging of blast-hole drill cuttings in order to perform knowledge-based mineralogical assessment. The use of these diagnostic tools increases the accuracy of ore-waste boundaries in resource block models, thus minimizing the uncertainty of ore grade estimates (Akbar et al., 2024).
Moreover, the physical limitations of the mining equipment should also be taken into account when building the grade-control blocks model. The operating parameters of the mine – including the height of benches, slope angles, and the capability of shovel-truck fleets – determine the practical dimensions of the SMU. The development of a discrete mining model, which combines the geological data with the mechanical limitations and efficient shovel-truck operations, enables mines to calculate the block dimensions which will ensure no dilution at all (Guo et al., 2024).
Finally, by incorporating proper sampling procedures, precise block modeling, and stochastic optimization, it is possible to achieve minimal dilution and misclassification of the mineral. Optimization approaches which account for geological uncertainty make it possible to improve planning of the life-of-mine production to optimize net present value of the business by taking care of the waste and tailings management (Adrien Rimélé et al., 2018). Only processing valuable minerals by the mill saves a lot of energy and increases profits.
References
Adrien Rimélé, M., Dimitrakopoulos, R., & Gamache, M. (2018). A stochastic optimization method with in-pit waste and tailings disposal for open pit life-of-mine production planning. Resources Policy, 57, 112–121. https://doi.org/10.1016/j.resourpol.2018.02.006
Akbar, S., Abdolmaleki, M., Ghadernejad, S., & Esmaeili, K. (2024). Applying Knowledge-Based and Data-Driven Methods to Improve Ore Grade Control of Blast Hole Drill Cuttings Using Hyperspectral Imaging. Remote Sensing, 16(15), 2823. https://doi.org/10.3390/rs16152823
Guo, W., Liu, G., Li, J., Chai, S., & Guo, S. (2024). Research on the method of determining the block size for an open-pit mine integrating mining parameters and shovel-truck’s operation efficiency. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-52815-9
