TRIM4Post-Mining brings together a consortium of European experts from industry and academia to develop an integrated information modelling system. This is designed to support decision making and planning during the transition from coal exploitation to a re-vitalized post-mining landscape enabling infrastructure development for agricultural and industrial utilization, and also to contribute to recover energy and materials from coal mining dumps.
TRIM4Post-Mining will develop efficient methods for comprehensive spatio-temporal data analytics, feature extraction and predictive modelling that allow for the identification of potential contamination areas and forecasting the waste dump dynamics. It will be founded up on a high-resolution spatio-temporal data-base utilizing state-of-the art multi-scale and multi-sensor monitoring technologies that characterize dynamical processes in coal waste dumps related to timely dependent deformation and geochemical processes.
This package addresses all activities related to technical, financial, and risk management of the project. It is also dedicated to effective dissemination actions. This WP does not provide data for the geodatabase.
This is an overarching work package that embeds all technology and methodological developments from WP3 to WP5 in a transparent communication framework for improved stakeholder engagement, awareness, and acceptance. Key issues to be addressed include social, environmental, financial, or cultural-based.
For the two case studies differences in terms of geography, environment, etc. will be analyzed to systematically derive overarching and site-specific issues that need to be addressed. This WP will deliver a set of Critical Revitalization Performance Indicators (RPI) which will use as a primary source, the developed geodatabase to communicate project value and risks to stakeholders for the test cases.
This package will define the potential for multi-scale and multi-sensor point and imaging monitoring technology for online geochemical waste characterization and contamination monitoring. It considers different sensors such as satellite images, Laser-induced Breakdown Spectroscopy (LIBS), X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FTIR), and an innovative non-destructive SONIC drill coring technology combined with Sonic-CPT technology. Each one of these elements is a source of data for the geodatabase.
Therefore, appropriate machine learning algorithms will be developed for feature extraction and fusion of different sensor data. The applications to be considered are face-mapping at the extraction and dumpsite, outcrop mapping, and drill-core profiling.
A data-driven spatio-temporal prediction method for inside-dump deformations and geochemical processes for both cases, lignite, and hard coal waste dumps will be developed. The basis will be already available operational, and long-term geometrical monitoring data combined with the documented material distribution inside the dump (3D Model) and newly acquired data as a result of WP3. All these data will be embedded and extracted from the designed geodatabase.
The actual information modelling system will be developed. It develops and implements a suitable software system for the collection and the processing of data from (smart) sensors, analysis, and modelling algorithms and second, it is focused on the design and the development of a VR/AR based visualization and interaction system to support an intuitive analysis of the data to provide powerful tools for decision making by all stakeholder groups. As in the previous WP, all data will be extracted from the designed geodatabase.
This package will integrate and demonstrate the technologies and methods developed within TRIM4Post-Mining.
| Title | Author | Link |
|---|---|---|
| Aspects of Nature and Species Conservation to Enhance Public Acceptability of Approval Procedures for Opencast mines | Reinhard Reißmann, Frank Schmidt | Download |
| TRIM4Post-Mining: Transition Information Modelling for Attractive Post-Mining Landscapes— A Conceptual Framework | Jörg Benndorf, et al. | Download |
| Utilization and analysis of digital mining dump models | Natalie Merkel, André John | Download |
| Hyperspectral Sensing to Boost AMD Monitoring in Post-Mining Scenery | Hernan Flores, Tobias Rudolph, Setfan Möllerherm | Download |
This project has received funding from the European Commission Research Fund for Coal and Steel under grant agreement No 899278.
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