This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
REAL-TIME MINING MOVING TOWARDS CONTINUOUS PROCESS MANAGEMENT IN MINERAL RESOURCE EXTRACTION
FUTURE MINING CONFERENCE
SYDNEY, 5TH OF NOVEMBER 2015
JÖRG BENNDORF, DELFT UNIVERSITY OF TECHNOLOGY
ON BEHALF OF THE REAL-TIME MINING CONSORTIUM
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
PARTNERS
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
BACKGROUND
Potential of critical raw materials in Europe classified by deposit sizes (PROMINE) 3
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
The main barriers to overcome for the successful economic exploitation:
o effective grade control, which will maximize resource potential along the whole value chain
o minimization of handling zero-value material introduced by
dilution, thus reducing unnecessary expenditure of energy and financial resources and
o management and control of the geological uncertainty due to
limited information available.
BACKGROUND
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
Main Source of Risk: Geological Uncertainty
Limited Information 1:10.000.000
Complex Geology
Tight product specifications
5 Foto: Neves Corvo Mine
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
THE TRADITIONAL APPROACH Mine Design Equipment Selection Reserve Estimation
Exploration and Data Collection
Resource Modelling
Production Scheduling and Operation
Processing and Sale
SILO 1 SILO 2 SILO 3 SILO 4 SILO 5
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
NEW INFORMATION POTENTIAL
Increasing Availability of Sensor Based Online Data:
• Material characterization (geo-chemical, textural and
physical properties)
• Equipment performance, upstream and downstream (e.g.
efficiency, down-time)
• Equipment location and material tracking (e.g. GPS, UPS) 7
Foto: MIBRAG
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
THE REAL-TIME MINING APPROACH
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
THE REAL-TIME MINING PROJECT OBJECTIVE Overall objective: to develop an innovative technical solution for resource-
efficient and optimal high precision/selective mining in geologically complex
settings using online data.
Hypothesis: recovery can be significantly increased by changing mineral
resource management from a ‘batch-type’ to a near-continuous model-based
controlled activity
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
REAL-TIME MINING BUILDING BLOCKS
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
EXTRACTION METHODS RTM CYCLIC EXTRACTION
Drill Hole
Core
Sample
Ore zone
Muck- pile
LHD
Ore- pass
Ore Transfer
BIN
Crusher
BIN
A
B
C
Control decision points
Sensors for material characterization
Sensors for machine performance
Sensors for geo-referencing (positioning and material tracking)
Selective Loading
Scheduling
Dispatching
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
REAL-TIME MINING REAL-TIME DATA
(Lead: RWTH Aachen)
Lead: TU Delft
Lead: SonicSampDrill 12
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
TEST CASE 1
“Reiche Zeche” Research Mine Freiberg, Germany Source: Description „Test Site Mine ‚Reiche Zeche‘, Freiberg, Saxony, Germany“ provided by TU Bergakademie Freiberg
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
TEST CASE 2
“Neves Corvo” Copper Mine Portugal (Massive sulphide ore and associated stockwork zone) Source: lundin mining
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
CASE STUDY UPDATING
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Sensor
Predict
Measure
Reconcile
GC Model
Grade Control (GC) Model
1. Reconcile – Extracted Blocks
2. Update – Scheduled Blocks
Extraction Performance
1. Improve production forecasts
2. Optimize control decisions
(proact)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
CASE STUDY UPDATING
Predicted Measurements
Actual Measurements
Reconciliation Software
Prior Block Model
Forward Prediction
Measurement File
Material Tracking/ Material
Flow Simulation in
the Mine
Updated Block Model
replace
Sensor
Source: Wambeke, T & Benndorf, J (2015). Data assimilation of sensor measurements to improve production forecast in resource extraction. In s.n. (Ed.), Proceedings of the 17th annual conference of the international association for mathematical geosciences, IAMG2015 (pp. 236-245). Houston: IAMG.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
Simulations
Mean Field
x SMU scale
x
CASE STUDY UPDATING
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
Simulations
Mean Field
x SMU scale
x
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CASE STUDY UPDATING
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
Optimal value
Optimal value
Acceptable range
Acceptable range
Penalty applied
Dai
ly p
rodu
ctio
n in
kt
Gra
de in
%
Forecast of process KPI’s for a given extraction schedule
CASE STUDY OPTIMIZATION
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
Simulation based optimization of extraction schedule to improve extraction performance
CASE STUDY OPTIMIZATION
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
ADVANCE WITHIN RTM
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
THREE TAKEAWAYS 1. Real-Time Mining is an exciting European Union funded
H2020 project and integrates multiple disciplines
2. Making best use of online production information can lead to a shift in paradigm from a batch-type to a continuous process monitoring and control and can create significant value
3. Real-Time Mining will demonstrate this hypothesis in full industrial scale case studies (TRL 6/7).
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641989
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Thank you for your attention and
Glückauf
www.realtime-mining.eu