The main target of the UNEXMIN project is to develop a fully autonomous submersible robot (UX-1) which can map flooded underground mines, and also deliver information about the potential raw materials of the mines. There are ca. 30 000 abandoned mines in Europe, from which many of them still could hold significant reserves of raw materials. Many of these mines are nowadays flooded and the latest information about them could be more than 100 years old. Although it is giving limited information, magnetic measurement methods, which detecting the local distortions of the Earth’s magnetic field can be very useful to identify raw materials in the mines. The source of the magnetic field which is independent of any human events comes from the Earths own magnetic field. The strength of this field depends by the magnetic materials in the near environment of the investigated point. The ferromagnetic materials have powerful effect to influence the magnetic field. In the nature, iron containing minerals, magnetite and hematite have the most powerful effect usually. The magnetic measurement methods are rapid and affordable techniques in geophysical engineering practice. For magnetic field strength and direction measurement FGM-1 sensors (manufactured by Speake & Co Llanfapley) were selected for the UX-1 robot. The sensor heads overall dimension are very small and their energy consumption is negligible. The FGM-1 sensor was placed and aligned in a plastic cylinder to ensure that the magnetic-axis aligned with the mechanical axis of the tube for more accurate measurement. There are 3 pairs of FGM-1 sensors needed for the proper determination of the current magnetic field (strength and direction). The position of sensor pairs need to be perpendicular compared to each other. The 3 pairs of FGM-1 sensors generate an arbitrary position Cartesian coordinate system. We further developed / had installed temperature sensors to all FGM-1 probes, to compensate the temperature dependency even though it has small effect. The UX-1 robot also contains the electronic block, which controls the three FGM-1 magnetic field sensor pairs, and store the measured data. The block contains the power module, the sensor interface modules with temperature compensation, the microcontroller module and the RS485 communication module also. The output data is a temperature compensated frequency value for each sensor pair. The measured magnetic signal from the local XYZ coordinate system (local for the UX-1) should be converted to a universal coordinate system during post processing of the data. The exact position, facing and inclination of the robot must be known in the whole dive time to be able to do the above conversion. The measured magnetic signal will be placed into the measured mine map, reconstructed from the delivered 3D point cloud, thus the exact location of the magnetic anomalies can be identified. Not much magnetic source is estimated in the operating environment of the robot, but its own generated magnetic noise can be significant. There will be many cooling fans, micro-controllers and multiple thrusters inside the pressure-hull of the UX-1, which generate magnetic field. The constant magnetic noise coming from the cooling fans can be compensated, but the varying fields caused by eg. the different thrusters’s speed is problematic. We design a calibration method, where the effect of the main thrusters (even with changing speed) and the effect of the constant cooling fans could be compensated. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 690008.

Mining operations rely on effective extraction policies, which base on concerted management and technical arrangements. In addition to commodities, mining of data is the increasingly matter of subject in mining engineering. The Horizon 2020 project – Real-Time-Mining supports the ongoing paradigm shift of pushing mining activities from discontinuous to continuous operation. In this respect, the partners TU Bergakademie Freiberg (TU BAF) and IBeWa Consulting tackle the issue of physical and logical data acquisition in underground mining. The first aspect of the project addresses the ‘logical’ provision of data. Mining technology is increasingly interacting among each other and integrated into globally distributed systems. At the same time, the integration of current mining devices and machineries into superordinated systems is still complex and costly. This means only a few number of mining operators is capable to integrate their operation technology into a Supervisory Control and Data Acquisition (SCADA) system. TU BAF presents the middleware OPC Unified Architecture, which is a platform independent middleware for data exchange and technology interconnection among distributed systems. By installing a SCADA demonstrator at the research and education mine Reiche Zeche, TU BAF intends to present the technical feasibility of a SCADA system basing on OPC UA even for SME mining operations. The second aspect of the project addresses the ‘physical’ provision of data via wireless transmission. The targeted use cases are mobile machineries and the surveillance of remote mine sites. Mobile machineries in underground mining are increasingly equipped with data management and autonomous operation systems. Correspondent data exchange to superordinated systems is mostly realized via Wireless Local Area Network (WLAN). A comprehensive WLAN signal coverage, however, is generally not maintained in under-ground mines due to lacking technical and economic feasibility. With the intention to in-crease the coverage/expense ratio at underground WLAN installations, TU BAF and IBe-Wa Consulting installed a WLAN test loop at Reiche Zeche mine basing on leaky feeder cables. Simultaneously, IBeWa Consulting pushes forward the surveilability of remote and/or hardly accessible mining sites by Through The Earth (TTE) data transmission. Current test performances present an enhanced stability for data transmission at ore / gneiss formations beyond 200m, primarily basing on a better alignment of the system to the isotropic characteristics of the bedrock.

This paper presents the development and quantification of a catalogue of Sustainable Performance Indicators (SPIs) for the assessment of the benefits real-time mining can offer in small and complex mechanised underground mining operations. The SPIs investigated in detail include: ‒ grade accuracy and error of the resource model, ‒ high/low grade ore classification accuracy and error, ‒ additional high grade ore identified per unit volume, ‒ profit expected per unit volume, ‒ ore classification accuracy per unit volume assigned to the stockpiles. A case study utilising the Red Lake gold mine located in Northwestern Ontario, Canada, which is owned by Goldcorp Inc., was designed with the aim to assess the effect of real time sensory data acquisition and resource model update on the SPIs. The methodology broadly comprises of three steps. Firstly, the provided dataset was used to develop a virtual asset model (VAM) representing the true 3D grade distribution in order to simulate the ‘sublevel cave and fill’ mining method and the associated grade data acquisition from the development drillholes and face monitoring, the development and production muck pile, LHD/scooptram and conveyor belt transport, taking into account the sensor parameters. Next, the acquired data was assimilated into the models developed for the purpose of detailed statistical assessment of the SPIs, thereby enabling optimised decision-making during the production of ore in order to meet the grade requirements. Finally, an evaluation of the sensor performance was carried out using three additional levels of sensor error and interpretation bias (10, 20 and 30%). The three models used for the quantification of the SPIs include: ‒ resource block model (RBM): which represents the 3D grade distribution in the ore body; ‒ grade control model: which enables selective stope production (drilling, charging and blasting) based on the underlying requirements pertaining to e.g. cut-off grade, time and economic constraints; and ‒ logistics model: which classifies the ore grades for conveyance and stockpiling, in order to eventually facilitate for the mixing of run of mine ore to meet the grade requirements before milling at the processing plant. The improvement of the SPIs when real time monitored data is used in the update of the models has been verified. It is also shown that the noise in the acquired data, which directly reflects both the accuracy and precision of the sensors, has a measurable effect on the values obtained for the SPIs. However, 10 to 20% noise does not appear to reduce significantly the improvements achieved, while 30% noise has a more profound effect on the SPIs and the quality of improvements achieved through real time data assimilation in the models. The work carried out demonstrates that there is a need for robust sensor technologies that allow for minimum bias in grade estimation and maximum classification accuracy. It is also expected that sensor performance is likely to vary from site to site and possibly within the same ore deposit mined due to local geological conditions (heterogeneity), variations in the underground environment were sensors are installed (affecting sensor performance), the mining method used (affecting the access and availability of real time monitored data) as well as the specifics of the sensor technologies used. Thus, it is suggested that sensor performance needs to be evaluated and quantified for the mine and area considered for sensor installation given the local geological, operational and mining method related characteristics and opportunities for monitoring.

The main objective of OPTIMORE is to optimize the crushing, milling and separation processing technologies for tungsten and tantalum. Optimization is realized by means of improved fast and flexible fine tuning production process control based on new software models, advanced sensing and deeper understanding of processes to increase yield and increase energy savings. The results explained in this work show this fulfilment with developed or simplified models for crushing, milling, gravity, magnetic and froth flotation separations. A new control system has been developed in this last part of the project, using the developed process models and advanced sensor systems. Validation of models in the simulation environment has been carried out. A pilot plant and real plant validation is planned for the end of the project. Knowledge transfer throughout the project between the Tungsten and Tantalum industry and the project partners has resulted in a strong relation between both which will continue to grow as the project concludes.

Real-Time Mining is a research and development project within the European Union\'s Horizon 2020 initiative and consists of a consortium of thirteen European partners from five countries. The overall aim of Real-Time-Mining is to develop a real-time framework to decrease environmental impact and increase resource efficiency in the European raw material extraction industry. The key concept of the research conducted is to promote a paradigm shift from discontinuous to a continuous process monitoring and quality management system in highly selective mining operations. The Real-Time Mining Control Cockpit is a framework for the visualization of online data acquired during the extraction at the mining face as well as during material handling and processing. The modules include the visualization of the deposit-model, 3D extraction planning, integrated data of the positioning-system as well as the visualization of sensor and machine performance data. Different tools will be developed for supporting operation control and optimized decision making based on real-time data from the centralized database. This will also integrate results from the updated resource model and optimized mine plan. The developed Real-Time Mining cockpit software will finally be integrated into a wider central control and monitoring station of the whole mine.

The project Viable Alternative Mine Operating System (¡VAMOS!) develops a new safe, clean and low visibility mining technique for excavating raw materials from submerged inland mines. During operations, the perception data of the mining vehicle can only be communicated to the operator via a computer interface. In order to assist remote control and facilitate assessing risks a detailed view of the mining process below the water surface is necessary. This paper presents approaches to real-time 3D reconstruction of the mining environment for immersive data visualisation in a virtual reality environment to provide advanced spatial awareness. From the raw survey data a more consistent 3D model is created using postprocessing techniques based on a continuous-time simultaneous localization and mapping (SLAM) solution. Signed distance function (SDF) based mapping is employed to fuse the measurements from multiple views into a single representation and reduce sensor noise. Results of the proposed techniques are demonstrated on a dataset captured in an submerged inland mine.

The application of sensor technologies for raw material characterization is rapidly growing, and innovative advancement of the technologies is observed. Sensors are being used as laboratory and in-situ techniques for characterization and definition of raw material properties. However, application of sensor technologies for underground mining resource extraction is very limited and highly dependent on the geological and operational environment. In this study the potential of RGB imaging and FTIR spectroscopy for the characterization of polymetallic sulphide minerals in a test case of Freiberg mine was investigated. A defined imaging procedure was used to acquire RGB images. The images were georeferenced, mosaicked and a mineral map was produced using a supervised image classification technique. Five mineral types have been identified and the overall classification accuracy shows the potential of the technique for the delineation of sulphide ores in an underground mine. FTIR data in combination with chemometric techniques were evaluated for discrimination of the test case materials. Experimental design was implemented in order to identify optimal pre-processing strategies. Using the processed data, PLS-DA classification models were developed to assess the capability of the model to discriminate the three material types. The acquired calibration and prediction statistics show the approach is efficient and provides acceptable classification success. In addition, important variables (wavelength location) responsible for the discrimination of the three materials type were identified.

This paper presents the development and implementation of a theoretical mathematical-statistical framework for sequential updating of the grade control model, based on a support vector machine learning algorithm. Utilising the Zambujal orebody within the Neves-Corvo Cu deposit in Portugal, parameters that can be measured in real time, used in visualisation, modelled for resource estimation, and used for process control visualisation and optimisation are considered. The methodology broadly comprises of three steps. Firstly, the provided dataset is used to develop a virtual asset model (VAM) representing the true 3D grade distribution in order to simulate the mining method. Then ore quality parameters are established simulating real time monitoring sensor installation at: (a) stope development and rock face monitoring (face imaging and drillholes); and (b) transport monitoring (muck pile, LHD/scooptram). Next, the acquired data was assimilated into the models as part of the sequential model update. Two different mining methods and the monitoring information that can be acquired during the ore extraction are analysed: (a) drift and fill mining and (b) bench and fill mining, which are widely implemented at the Neves-Corvo mine. Selected study zones were chosen such as to contrast mining through the high/low grade zones with different degrees of heterogeneity, which demonstrate the performance of resource estimation and classification models developed in heterogeneous mining stopes. The grade accuracy and error in the resource model, and high/low grade ore classification accuracy and error are evaluated as performance metrics for the proposed methods. In drift and fill mining, drillhole and face sampling data collection was simulated in a real-time manner and fed into the support vector machine (SVM) regressor to update the resource estimation model in both a high grade and low grade drift scenarios. In each scenario, six drift and fill mining steps were simulated sequentially and the posterior resource models, after integrating real time mining data, have shown significant improvement of bias correction in both updating planned resources and reconciling extracted ore. In bench and fill mining, grade classification based on random sampling data from muck pile was demonstrated, considering scoop by scoop derived monitoring data. Three different classifiers (mean, median, and Bayesian) were tested and shown very good performance. In the case study presented here, a sequence of 15 blasting steps was simulated with each step requiring 112 scooping operations to transport the blasted ore. Using the real time monitored information, it was shown that at each blasting step over 85% of the scoops can be labelled correctly using the proposed methods and with an accuracy of over 95%.

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.