Abstract
A key requirement for the mining industry is the characterization of the spatial distribution of geometallurgical properties of the ore and waste in a mineral deposit. Due to geological uncertainty, resource models are crude representations of reality, and their value for forecasting is limited. Information collected during the production process is therefore of high value in the mining production chain. Models for mine planning are usually based on exploration information from an initial phase of the mineral extraction process. The integration of data with different supports into the resource or grade control model allows for continuous updating and is able to provide estimates that are more accurate locally. In this paper, an updating algorithm is presented that integrates two types of sensor information: sensors characterizing the exposed mine face, and sensors installed in the conveyor belt. The impact of the updating algorithm is analysed through a case study based on information collected from Reiche-Zeche, a silver–lead–zinc underground mine in Freiberg, Germany. The algorithm is implemented for several scenarios of a grade control model. Each scenario represents a different level of conditioning information prior to extraction: no conditioning information, conditioning information at the periphery of the mining panel, and conditioning information at the periphery and from boreholes intersecting the mining panel. Analysis is performed to compare the improvement obtained by updating for the different scenarios. It becomes obvious that the level of conditioning information before mining does not influence the updating performance after two or three updating steps. The learning effect of the updating algorithm kicks in very quickly and overwrites the conditioning information.
Highlights
In mineral resource extraction, the main objective is meeting production targets in terms of ore tonnage, mineral content and grades
Different visualizations characterize the statistical moments and show the improvements achieved by the data assimilation implementation
Box plots represent the evolution of the distribution of each selective mining unit (SMU) for every drift
Summary
The main objective is meeting production targets in terms of ore tonnage, mineral content and grades This contributes to the optimal utilization of the mill with regard to throughput and metal recovery, while minimizing costs. The available information consists of exploration holes augmented by chip or channel samples from the grade control process This database is spatially sparse, and hampered by time delay, as samples are typically analysed offline in a laboratory, and the quality is lower. It can take several days before results are available; the SMU may have already been mined, with decisions about its destination/processing based on an outdated model.
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