Grain Size Distribution of Copper Ore as Means for Qualitative Evaluation of Its Lithological Composition

Abstract

Information about the grain size distribution of a material plays an important role in optimizing production systems in various branches of industry. Analysis of particle size distribution in the run-of-mine material is frequently employed to verify the quality of drilling and blasting operations. Adequate software and improved grain size identification technologies not only help to monitor and evaluate the results of blasting operations, but also inform the selection of blasting methods which best correspond to particular geological and mining conditions both in surface mining and in underground mining. The accuracy of indirect, image-analysis methods used in the identification of grain size distribution motivated some pilot works aimed at using the grain size to evaluate the quality of a lithological complex copper ore deposit. Describing the run-of-mine material fed to the Ore Enrichment Plant (OEP) with the use of its grain size distribution may improve the techniques currently employed to optimize the energy efficiency of ore treatment processes. A model of ore flow in the underground transportation system, developed in the FlexSim environment, as part of the DISIRE research project, may prove a valuable optimization tool. This paper presents the results of preliminary research aimed at verifying whether grain size distribution of the run-of-mine material correlates with its lithological composition. The examinations covered grain size distribution in copper ore transported on belt conveyors in two mines in which the extracted ore has different lithology. The research was performed with the use of photogrammetric techniques and the Split Desktop 4.0 computer application. The advantage of the proposed technique is that it can be used at any location in the mine. The analysis was performed on the photographic material collected in situ at the “Lubin” mine. This material was supplemented with qualitative data stored in the Run-Of-Mine Ore Monitoring system (further: MOPRONA), as defined on the basis of channel samples collected on the day of tests.