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Real-Time-Mining Report Summary

Project ID: 641989
Funded under: H2020-EU.3.5.3.

Periodic Reporting for period 1 - Real-Time-Mining (Real-time optimization of extraction and the logistic process in highly complex geological and selective mining settings)

Reporting period: 2015-04-01 to 2016-09-30

Summary of the context and overall objectives of the project

The overall aim of Real-Time-Mining is to develop a real-time mine control framework to decrease environmental impact and increase resource efficiency in the European raw material extraction industry. The key concept of the proposed research promotes the change in paradigm from discontinuous intermittent process monitoring to a continuous process and quality management system in highly selective mining operations. Real-Time Mining is developing a real-time process-feedback control loop linking data acquired online during extraction rapidly with a sequentially updatable resource model associated with real-time optimization of long-term planning, short-term sequencing and production control decisions. The main over-arching project objective is integrating the different components of autonomous positioning, spatially-referenced real-time sensor-based monitoring, extraction planning model updating together with decision and machine control optimization.
The impact of the project is expected on the environment through a reduction in CO2-emissions, increased energy efficiency and production of zero waste by maximizing process efficiency and resource utilization. Currently economically marginal deposits or difficult to access deposits will be become industrial viable. This will result in a sustainable increase in the competitiveness of the European raw material extraction through a reduced dependency on raw materials from non-EU sources.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

"Real-Time-Mining includes research and demonstration activities towards integrating automated sensor based material characterization, online machine performance measurements, underground navigation and positioning, underground mining system simulation and optimization of planning decisions, state-of-the art updating techniques for resource/reserve models. The project has progressed according to plan with the foundational step of requirements analysis completed for all aspects. Research and prototyping has advanced considerably and some components have gone through initial phases of testing.

During demostration activities the developed technology will be benchmarked against current indursty pracice using a set of Sustainable Performance Indicators (SPIs). For defining SPIs and the new technology environment a generic process map of small scale mining operations has been compiled and identified numerous opportunities for sensor deployment and decision points along the mining value chain from the production face through extraction and haulage to the products delivered to downstream processing. Sustainability and industrial viability metrics have been proposed based on quantitative approaches developed with life cycle assessment research, expressing impact relative to product units, i.e., metal extracted. This has been supported by a review of relevant frameworks and policies, quality management and environment management systems, best practices and benchmarks.

Regarding sensor data acquisition, requirement specifications have been completed and solution options reviewed and evaluated. Some test measurement programmes have been performed with consequent data analysis and research and development into data modelling approaches for extracting relevant information. Some sensor prototypes have been developed and deployed during test measurements for characterizing prioritized minerals and elements using the following sensor technologies; SWIR, FTIR, Optical camera, thermal camera, hyperspectral imager, LIBS and XRF. Lessons learnt are being carried forward into further improvement and development. A sensor combination concept has been developed based on LIBS/ Raman integration on the same platform. Preliminary tests were carried out to

Machine performance monitoring and inference of rock strength properties for rock cutting applications has progressed by identifying suitable sensor options and testing of vairous configuration options within a controlled laboratory environment Cutting tests with different artificial rocks are on-going and aim at improving the understanding of the physics of the process and finding an optimal measurement configuration. For drilling applications supervised learning methods have been investigated to establish the link between multiple inter-correlated machine performance sensor signals and rock strength properties. These investigated methods appear approriate however no conclusive results could yet be established due to the small size and high level of noise in the test data set. Future work will focus on generating a larger and better controlled data set.

For underground positioning system requirements have been specified and a prototype developed for performing test measurements. These are being analysed with the aim of data validation and integration of the three utilized positioning technologies. Wireless VLF communication has been tested at Freiberg test mine and preliminary findings have been included in the forward work plan.

Regarding sequential reource model updating and mine planning optimization, requirements and interfaces have been defined, and a review of suitable techniques has been performed. Virtual Asset models have been generated for three deposits, providing an environment for development and testing. Two resource model updating solutions have been implemented and tested in these environments; a third approach is still in development. Approaches for long- and short-term mine planning optimization have been implemented, with some testing and improvements still ongoing. A conceptual approach for a meta-heuristic (generic algorithms) stope optimizer has been developed and implemented taking into account geological uncertainty: an alternative approach has also been developed and includes stope optimization and sequencing. 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.

Regarding the central data management component, requirements and interfaces have been defined and are being implemented and tested using a test data set compiled with inputs from all partners. Requirements for a state-of-the-art advanced mine control, operation and safety system for small scale mines have been defined and existing commercial solutions reviewed. A data flow diagram and a list of specific file formats has been established. State of the art data formats have been selected to ensure high system performance. Interfaces and system architecture for the data management framework (DMF) were defined. The connection between computed Data and the DMF will be archived with an Integration Client implemented in C#. All interfaces have been implemented as prototypes with testing on-going. The 3D visualization cockpit components have been defined and development libraries to be used for the cockpit have been selected."

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Real-Time-Mining is developing a novel framework using data derived from sensors for real-time-process reconciliation and optimization for mineral resource extraction processes in highly complex geological and selective mining settings. The projected project impacts stated in the project proposal remain valid and appear achievable. These impacts include a reduction in CO2-emissions, increased energy efficiency and production of zero waste by maximizing process efficiency and resource utilization. Currently economically marginal deposits or difficult to access deposits will be become industrial viable. This will result in a sustainable increase in the competitiveness of the European raw material extraction through a reduced dependency on raw materials from non-EU sources.
Underground positioning technology has advanced through a prototype instrument fusing multiple technologies and initial testing indicates that a reliable and robust positioning system will be achieved. A novel LIBS platform for deployment underground has been designed and a construction and testing plan developed. Mapping mine faces via lithological classification of optical images has attracted interest of industry partners already and combination with thermal and infra-red imaging will further enhance this capability. Hardness characterization in rock-cutting and Sonic drilling has been linked to characteristic sensor signals with promising prospects for regression modelling. The implementation of the data management framework and visualization cockpit is advancing and the delivery of a cost efficient solution for small scale mines appears achievable. An innovative resource model update algorithm utilizing Kalman-Filter-techniques is in place for the univariate case and is being extended for modelling multiple correlated variables utilizing mine face imagery and supply chain data. Mathematical programming based algorithms for long-term underground mine planning have been developed and account for geological uncertainty and waste backfilling.

Related information

Record Number: 198061 / Last updated on: 2017-05-16
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