Resource model updating for underground mining production settings


Resource model updating for underground mining production settings

Prior, A.; Benndorf, J.; Mariz, C.

This research is part of the European Union funded 'Real Time Mining' project, which aims to develop a new framework to reduce uncertainties during the extraction process in highly selective underground mining settings. A continuously self-updating resource/grade control model concept is presented and aims to improve the raw material quality control and process efficiency of any type of mining operation. Applications in underground mines include the improved control of different components of the mineralogy and geochemistry of the extracted ore utilizing available “big data” collected during production. The development of the methodology is based on two full scale case study, the copper-zinc mine Neves-Corvo in Portugal and Reiche-Zeche mine in Germany. These serve for both, for the definition of method requirements and also as a basis for defining a Virtual Asset Model (VAM), which serves for artificial sampling as benchmark for performance analysis. This contribution introduces to the updating concept, provides a brief description of the method, explains details of the test cases and demonstrates the value added by an illustrative case study.

Keywords: Underground Mining; Data Assimilation; Geostatistitcs

  • Open Access Logo Contribution to proceedings
    REAL TIME MINING - Conference on Innovation on Raw Material Extraction Amsterdam 2017, 10.-11.10.2017, Amsterdam, The Netherlands

Permalink: https://www.hzdr.de/publications/Publ-28854
Publ.-Id: 28854