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.

Efficient data collection, analysis and preservation are needed to accomplish adequate business decision making. Long-lasting and sustainable business operations, such as mining, add extra requirements to this process: data must be reliably preserved over periods that are longer than that of a typical software life-cycle. These concerns are of special importance for the combined on-line-on-mine-real-time expert system SOLSA (http://www.solsa-mining.eu/) that will produce data not only for immediate industrial utilization, but also for the possible scientific reuse. We thus applied the experience of scientific data publishing to provide efficient, reliable, long term archival data storage. Crystallography, a field covering one of the methods used in the SOLSA expert system, has long traditions of archiving and disseminating crystallographic data. To that end, the Crystallographic Interchange Framework (CIF, [1]) was developed and is maintained by the International Union of Crystallography (IUCr). This framework provides rich means for describing crystal structures and crystallographic experiments in an unambiguous, human- and machine- readable way, in a standard that is independent of the underlying data storage technology. The Crystallography Open Database (COD, [2]) has been successfully using the CIF framework to maintain its open-access crystallographic data collection for over a decade [3,4]. Since the CIF framework is extensible it is possible to use it for other branches of knowledge. The SOLSA system will generate data using different methods of material identification: XRF, XRD, Raman, IR and DRIFT spectroscopy. For XRD, the CIF is usable out-of-the-box, since we can rely on extensive data definition dictionaries (ontologies) developed by the IUCr and the crystallographic community. For spectroscopic techniques such dictionaries, to our best knowledge, do not exist; thus, the SOLSA team is developing CIF dictionaries for spectroscopic techniques to be used in the SOLSA expert system. All dictionaries will be published under liberal license and communities are encourage to join the development, reuse and extend the dictionaries where necessary. These dictionaries will enable access to open data generated by SOLSA by all interested parties. The use of the common CIF framework will ensure smooth data exchange among SOLSA partners and seamless data publication from the SOLSA project.

Short-term mine plans are the key operational basis for ore production targets ranging from shift to weekly or monthly targets. Short-term plans cover detailed operational subprocesses such as development, extraction and backfill schedules as well as materials handling and blending processes. The aim is to make long-term goals feasible by providing a constant plant feed that complies with quality constraints. Short-term mine planning highly depends on the accuracy of the resource model as well as the current production status and equipment fleet. Most of these parameters are characterized by uncertainties due to a lack of information and equipment reliability. At the same time, concentrate production and quality must be kept within acceptable ranges to ensure productivity and economic viability of the operation. Within the EU-funded Real-Time Mining project, the reduction of uncertainty in mine planning is carried by using real-time data. Ore and rock characteristics of active faces and equipment data are iteratively integrated in a simulation-based optimization tool. Therefore, predicted processing plant efficiencies can be met by delivering constant ore grades. Hence, a constant concentrate quality is ensured and long-term targets can be fulfilled. Consequently, a more reliable exploitation plan of the mineral reserve is facilitated.

This paper proposes a system for the ingestion and analysis of real-time sensor and actor data of bulk materials handling plants and machinery. It references issues that concern mining sensor data in cyber physical systems (CPS) as addressed in O’Leary et al. [2015].

Recent development of hyperspectral snapshot cameras offers new possibilities for ore analysis. A method for generating a 3D dataset from RGB and hyperspectral images is presented. By using Structure from Motion, a reference of each source image to the resulting point cloud is kept. This reference is used for projecting hyperspectral data onto the point cloud. Additionally, with this work flow it is possible to add meta data to the point cloud, which was generated from images alone.

In mining operations, the time delay between grade estimations and decision about the scheduling of stopes mining can result in seriously outdated information and, consequently, a substantial mined reserves bias. To mitigate this gap between the grade estimation of an orebody and its exploitation, this paper proposes a new method of speedily updating resources and reserves integrated into the concept of real-time mining. This consists in the continuous and swift update of mine reserves, which requires a continuous and fast stream of the measurements of stopes in an underground mine rather than the chemical lab analysis of core samples or chip/face samples. Here we propose using portable for the swift monitoring of ore grades. However, this “fast” data be highly uncertain. For this reason, the first step consists of creating a bidistribution function between “uncertain” XRF and the corresponding “hard” measurements, based on empirical historical data. Following this, the uncertainty of the XRF measurements is derived from those bi-distributions through the conditional distribution of real values given to the known XRF measurement.The second step involves updating the reserves by integrating this uncertain XRF data, which has been quantified by conditional distributions, in the grade characterization models. For this purpose, a stochastic simulation with point distributions is applied. A case study of a sulphide copper deposit illustrates the proposed methodology.

Mobile laser scanning systems automate the acquisition of 3D point clouds of environments. The mapping systems are commonly mounted on cars or ships. This paper presents a flexible mapping solution mounted on an underground vehicle that is able to map underground mines in 3D in walking speeds. A clever choice of hard- and software enables the system to generate 3D maps without using GPS (global positioning system) information and without relying on highly expensive IMU (inertial measurement unit) systems.