Periodic Reporting for period 3 - Real-Time-Mining (Real-time optimization of extraction and the logistic process in highly complex geological and selective mining settings)
Berichtszeitraum: 2018-04-01 bis 2019-03-31
The objective of REAL-TIME MINING was the development and integration of components for positioning, real-time sensor-based monitoring, extraction planning model updating and decision and machine control optimization. As an integrated technical solution it aimed at growing European capability for resource-efficient and optimal high precision/selective mining in geologically complex settings. The fundamental philosophy of the research promoted the change in paradigm from discontinuous intermittent process monitoring to a continuous process and quality management system. The project addressed the diverse technical areas of:
- sensor data acquisition for material characterization, machine performance measurements and underground navigation and positioning
- updating techniques for resource models and underground mining system optimization
- central data management system and 3D visualization
- sensor data acquisition for material characterization, machine performance measurements and underground navigation and positioning
- updating techniques for resource models and underground mining system optimization
- central data management system and 3D visualization
The overall project objectives have been achieved and respective prototypes and solutions have been integrated in the Real-Time Mining framework during the demonstration activities.
Sustainability and industrial viability metrics for the assessment of the benefits real-time mining can offer in small and complex mechanised underground settings have been developed, quantified and validated in different geological settings and for different mining methods. A review of frameworks and policies, quality management and environment management systems, best practices and benchmarks relevant for the project context has been performed. This enabled identification of gaps and opportunities for further indicator development and standardisation.
Underground positioning faces challenges due to lack of GPS data and the underground environment. A positioning prototype has been built, integrating three positioning technologies, ultra-wideband, IMU and laser ranging. Sensor fusion was implemented based on a particle filter approach. The required accuracy of half a meter for a 2D-Positioning system was achievable and clearly show the potential of this system. With minor adjustments to the hardware, especially the antenna design, and the particle filter, accuracies below 0.5 m are possible. Also tested was a system for wireless VLF communication.
Sensor technologies for geochemical and mineralogical material characterization operate over a wide range of the electromagnetic spectrum. Suitable sensor solutions for the test case materials included visual and hyperspectral imaging, mid-wave and long-wave infrared, Laser Induced Breakdown Spectroscopy (LIBS), thermal imaging and Raman spectroscopy. Underground measurements were combined with lab validation using state of the art methodlogies. The sensor technologies proved usable for the characterization of a polymetallic sulphide ore deposit, enabling identification, prediction and classification of the raw materials. Predictive models using MWIR and LWIR spectra allow discrimination of ore and waste materials and indicate elemental concentrations.
A LIBS system and analysis software were developed and enabled qualitative and semi-quantitative analysis of most of the test case elements during field validation. Thermal imaging was researched in lab and field and a promising material classification methodology was achieved.
Unconstrained compression strength and tensile strength are key raw material properties required for blast design and local rock support strategies. Machine and acustic emission data was acquired during a field program, drilling a series of rock types by sonic drilling. Supervised learning models delivered predictions for the target parameters with about 10% relative error.
Cutting tests with different artificial rocks has improved the understanding of the physics of the process.
Methods allowing for fast incorporation of online sensor data into resource models were identified, implemented and tested extensively.
Particular methods developed have been:
- A filtering algorithm for big data handling and analysis
- Sequential resource/grade control model updating (ensemble – kalman filter, locally weighted kernel based regression, direct sequential simulation)
- Scenario reduction algorithm.
A stope optimizer has been developed and implemented taking into account geological uncertainty. Discrete event simulation approaches have been investigated and a concept developed for production control model updating and early identification of production targets being at risk. A scheduling method for the short-term mine plan has been developed, and Discrete Event simulation model set up for production control. A test campaign with smart tags for material tracking at the mine site was conducted for supporting simulation data with actual material flow data.
Real time local rock support has been investigated; optimization concepts for Drill & Blast and Rock-Bolting were evaluated.
Integration and visualization was achieved by developing a number of software modules and their integration to achieve a complete system of the project:
- Framework and prototype for Mine Control Cockpit application
- Interfaces to different file formats
- Interface to GIS database
- Different functionalities for intuitive data exploration in 3D
- Integration of proprietary VR framework
The developed technology ranges in Technology Readiness Level from 4 - 7. Exploitation plans and market entry strategies have been developed for the more mature components. These include:
- LIBS Technology,
- LSA Analysis Software,
- Rapid resource model updating algorithms
- Real-time Mining Cockpit, and
- Smart Measurements While Drilling (MWD) System.
A joint venture that combined results from sensor research, the data management and visualization and resource model updating appears promising and will be further assessed. This combination would unravel the full added value of online sensor technology and could be implemented in any operation in modular fashion.
Sustainability and industrial viability metrics for the assessment of the benefits real-time mining can offer in small and complex mechanised underground settings have been developed, quantified and validated in different geological settings and for different mining methods. A review of frameworks and policies, quality management and environment management systems, best practices and benchmarks relevant for the project context has been performed. This enabled identification of gaps and opportunities for further indicator development and standardisation.
Underground positioning faces challenges due to lack of GPS data and the underground environment. A positioning prototype has been built, integrating three positioning technologies, ultra-wideband, IMU and laser ranging. Sensor fusion was implemented based on a particle filter approach. The required accuracy of half a meter for a 2D-Positioning system was achievable and clearly show the potential of this system. With minor adjustments to the hardware, especially the antenna design, and the particle filter, accuracies below 0.5 m are possible. Also tested was a system for wireless VLF communication.
Sensor technologies for geochemical and mineralogical material characterization operate over a wide range of the electromagnetic spectrum. Suitable sensor solutions for the test case materials included visual and hyperspectral imaging, mid-wave and long-wave infrared, Laser Induced Breakdown Spectroscopy (LIBS), thermal imaging and Raman spectroscopy. Underground measurements were combined with lab validation using state of the art methodlogies. The sensor technologies proved usable for the characterization of a polymetallic sulphide ore deposit, enabling identification, prediction and classification of the raw materials. Predictive models using MWIR and LWIR spectra allow discrimination of ore and waste materials and indicate elemental concentrations.
A LIBS system and analysis software were developed and enabled qualitative and semi-quantitative analysis of most of the test case elements during field validation. Thermal imaging was researched in lab and field and a promising material classification methodology was achieved.
Unconstrained compression strength and tensile strength are key raw material properties required for blast design and local rock support strategies. Machine and acustic emission data was acquired during a field program, drilling a series of rock types by sonic drilling. Supervised learning models delivered predictions for the target parameters with about 10% relative error.
Cutting tests with different artificial rocks has improved the understanding of the physics of the process.
Methods allowing for fast incorporation of online sensor data into resource models were identified, implemented and tested extensively.
Particular methods developed have been:
- A filtering algorithm for big data handling and analysis
- Sequential resource/grade control model updating (ensemble – kalman filter, locally weighted kernel based regression, direct sequential simulation)
- Scenario reduction algorithm.
A stope optimizer has been developed and implemented taking into account geological uncertainty. Discrete event simulation approaches have been investigated and a concept developed for production control model updating and early identification of production targets being at risk. A scheduling method for the short-term mine plan has been developed, and Discrete Event simulation model set up for production control. A test campaign with smart tags for material tracking at the mine site was conducted for supporting simulation data with actual material flow data.
Real time local rock support has been investigated; optimization concepts for Drill & Blast and Rock-Bolting were evaluated.
Integration and visualization was achieved by developing a number of software modules and their integration to achieve a complete system of the project:
- Framework and prototype for Mine Control Cockpit application
- Interfaces to different file formats
- Interface to GIS database
- Different functionalities for intuitive data exploration in 3D
- Integration of proprietary VR framework
The developed technology ranges in Technology Readiness Level from 4 - 7. Exploitation plans and market entry strategies have been developed for the more mature components. These include:
- LIBS Technology,
- LSA Analysis Software,
- Rapid resource model updating algorithms
- Real-time Mining Cockpit, and
- Smart Measurements While Drilling (MWD) System.
A joint venture that combined results from sensor research, the data management and visualization and resource model updating appears promising and will be further assessed. This combination would unravel the full added value of online sensor technology and could be implemented in any operation in modular fashion.
During the project, 9 PhD candidates have advanced their studies and the SME beneficiaries have grown, with increases in turnover by up to 200% and increased headcount by a total of 15 and additional planned near-term recruitment.
The main impact of the project outcomes originates from significantly improved orebody knowledge in real time enabling improved decision making. The resulting impacts are manifold and include:
- Increased metal and value recovery and high resource utilization
- Improved material dispatching decisions
- Better environmental management and decisions
- more efficient ore beneficiation
All above points decrease the operational unit costs, making mining more efficient. This potentially leads to the uptake of mining activities in currently economically marginal deposits in Europe. Consequences include a higher security in supply with critical raw materials in Europe and jobs created in the mineral sector. In addition, innovative technical solutions developed within Real-Time Mining will eventually find their way to the global raw material market. European SME’s could take a leadership in this new market segment.
The main impact of the project outcomes originates from significantly improved orebody knowledge in real time enabling improved decision making. The resulting impacts are manifold and include:
- Increased metal and value recovery and high resource utilization
- Improved material dispatching decisions
- Better environmental management and decisions
- more efficient ore beneficiation
All above points decrease the operational unit costs, making mining more efficient. This potentially leads to the uptake of mining activities in currently economically marginal deposits in Europe. Consequences include a higher security in supply with critical raw materials in Europe and jobs created in the mineral sector. In addition, innovative technical solutions developed within Real-Time Mining will eventually find their way to the global raw material market. European SME’s could take a leadership in this new market segment.