RTRO–Coal: Real-Time Resource-Reconciliation and Optimization for Exploitation of Coal Deposits

This contribution presents an innovative and integrated framework for real-time-process reconciliation and optimization (RTRO) in large continuous open pit coal mines. RTRO-Coal is currently developed, validated, tested and implemented as part of a multi-national multi-partner European Union funded R&D project. The key concept is to promote a shift in paradigm from intermittent discontinuous to a continuous process monitoring and quality management system in large scale coal mining operations. The framework is based on a real-time feedback control loop linking online data acquired during extraction rapidly with a sequentially up-datable resource model. The up-to-date model is integrated with a real-time optimization of short-term sequencing and production control decisions. Improved decisions are expected to lead to increased resource-and process efficiency and support a sustainable extraction of natural resources. This contribution introduces to the framework, discusses main building blocks and illustrates the value added by the means of selected examples.

[1]  G. Panagiotou,et al.  Discrete-Event Simulation of Continuous Mining Systems in Multi-layer Lignite Deposits , 2015 .

[2]  Roman Kapica,et al.  Global navigation satellite system (GNSS) technology for automation of surface mining , 2011 .

[3]  J. Benndorf,et al.  Stochastic long-term production scheduling of iron ore deposits: Integrating joint multi-element geological uncertainty , 2013, Journal of Mining Science.

[4]  Thys B Johnson,et al.  OPTIMUM OPEN PIT MINE PRODUCTION SCHEDULING , 1968 .

[5]  J. Benndorf,et al.  Planning for Reliable Coal Quality Delivery Considering Geological Variability:A Case Study in Polish Lignite Mining , 2015 .

[6]  R. Dimitrakopoulos,et al.  Recent applications of operations research and efficient MIP formulations in open pit mining , 2004 .

[7]  Dieter Gärtner,et al.  Market-Oriented, Flexible and Energy-Efficient Operations Management in RWE Power AG’s Opencast Mines , 2015 .

[8]  Accuracy Assessment of Geostatistical Modelling Methods of Mineral Deposits for the Purpose of their Future Exploitation - Based on One Lignite Deposit , 2012 .

[9]  J. Benndorf,et al.  Making Use of Online Production Data: Sequential Updating of Mineral Resource Models , 2015, Mathematical Geosciences.

[10]  Abhijit Gosavi,et al.  Simulation-based optimisation for material dispatching in Vendor-Managed Inventory systems , 2007, Int. J. Simul. Process. Model..

[11]  Roussos Dimitrakopoulos,et al.  Generalized Sequential Gaussian Simulation on Group Size ν and Screen-Effect Approximations for Large Field Simulations , 2004 .

[12]  Mark Gershon,et al.  Optimal mine production scheduling: evaluation of large scale mathematical programming approaches , 1983 .

[13]  J. Costa Simulation — an approach to risk analysis in coal mining , 2002 .

[14]  Marcelo Moretti Fioroni,et al.  Simulation of continuous behavior using discrete tools: Ore conveyor transport , 2007, 2007 Winter Simulation Conference.

[15]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[16]  J. L. Roux An Introduction to the Kalman Filter , 2003 .

[17]  J. Benndorf,et al.  Performance optimization of complex continuous mining system using stochastic simulation , 2014 .

[18]  J. Benndorf Application of efficient methods of conditional simulation for optimising coal blending strategies in large continuous open pit mining operations , 2013 .

[19]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[20]  G. N. Panagiotou,et al.  Simulation of a continuous lignite excavation system , 2005 .

[21]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[22]  Wolfgang Nowak,et al.  Parameter Estimation by Ensemble Kalman Filters with Transformed Data , 2010 .

[23]  J. Benndorf,et al.  Optimizing of Long-Term Mine Planning in Large Lignite Deposits , 2014 .

[24]  G. Reklaitis,et al.  A simulation based optimization approach to supply chain management under demand uncertainty , 2004, Comput. Chem. Eng..