coupling simulation of mineral processing with life cycle...
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Coupling simulation of mineral processing with Life Cycle Assessment
To assess the environmental impacts of copper production
COORDINATED BY
8th International Conference on Life Cycle ManagementLCM 2017, 3-6 Sept 2017, Luxembourghttp://lcm-conferences.org/
Speakers: Antoine Beylot (BRGM) Augustin Chanoine (Deloitte sustainability)
Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
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Focus of our presentation
Context and objectives of the studyIn Europe, most of the primary sources w/ high or moderate grades, reasonable accessibility and that are easy to process are exhausted. Primary resources: still available resources are polymetallic, lower-grade ores
Secondary resources: mining waste contains residual quantities of valuable metals
Focus on copper: need for alternative extraction processes of copper. Bio-hydrometallurgical technologies have the potential to: 1/ better adapt to lower-grade ores, 2/ extract metals in copper mining waste and 3/ lower the environmental impact of the mining industry
Objective of German-French EcoMetals project: to develop bioleaching, pretreatment and metal recovery techniques for copper extraction and demonstrate their efficiency, profitability and sustainability
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Pretreatment BioleachingMetal
RecoveryRun-of-Mine Concentrate
Pregnant Liquid
SolutionCopper Cathode
Aqueous phase (15% Cu + other
metals)
~1% Cu 13,7% Cu 25,7 g Cu/L 100% Cu
Copper precipitate
Recoverable metals (Ni, Co, Zn)
Solid waste: recoverable metals
(Lead, Silver)
Iron recovery
Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
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Process chain environmental impacts
“Coupling” process simulation with LCA: concept
Building the model on a Case Study
Coherent material balance calculationModel calibration
Operational and experimental data
- Reconciliated mass balances- Intermediate exchanges- Elementary flows
LCA + LCC
Life Cycle Inventory modellingEnvironmental impact and economic assessment
Scenarios Modelling
Modification of input parameters
Redesigning the process
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Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
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Scope of the study
Case study: exploitation of a Kupferschiefer copper ore at Lubin mine (Poland)
Lithotypes and chemical composition• Carbonates: 17,6 wt% in share, with 1,50% Cu• Shale: 13,2 wt% in share, with 2,78% Cu• Sandstone: 69,2% wt% in share, with 0,91% Cu
Functional Unit• To produce 1 ton of Cu in 13,7% Cu concentrate
System boundary• From Run-of-Mine (RoM) ore to copper concentrate
Life Cycle Inventory dabatase• ecoinvent 3.3
Environmental impact categories• Selection of a restricted list of 6 mid-point impact categories assessed with recognized
characterization methods: UseTox 2 for toxicity indicators + latest PEF recommendations for other impact categories
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Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
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Model construction: Mass Balances
Initial raw data on mass balances• On-site operational data completed
with hypotheses
• Global mass flows and substance flows (Cu, Corg and Cinorg)
• Inconsistent mass balances:
Mass in ≠ Mass out
Reconciliation of mass balances • i.e. finding estimators which are:- Consistent with mass balance constraints- Close to initial values, as a function of the data accuracy
• Lowering the uncertainty of global mass balances by benefiting from the higher accuracy on substance flows
Water
Tailings
W1 –Classification &
Grinding
W0 – ROM crushing & screening
W3 – Shale/ Carbonate
Beneficiation
Concentrate thickener
Tailings thickener
W2 –Sandstone
BeneficiationConcentrate
Recycled water
RoM
Recycled water
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Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
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Model construction: Implementing standard models
Energy consumption• Bond Formula, as a function of:
Crushability of lithotypes
Particle size distribution
Steel and reagents consumption• Steel as a function of abrasion indices of lithotypes
Data from UVR (German partner in Ecometals project)
Air emissions• Dust and CS2 emission factors drawn from the literature
Descriptive models
≠ Predictive
models
Lithology Shale Carbonate Sandstone
Work Index (kWh/t) 16,2 7,6 20,2
Abrasion Index (kg/kWh)
0,02 0,32 0,6
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Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
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Case study: Inventory of inputs and outputs
W1 – Ball Mill 1
Solid flow: 1285 t/hrd80 ≈ 1500 microns1,12 wt% of Cu
Electricity: 7,69 kWh/t ore
Steel: 1,09 kg/t ore
Solid flow: 1285 t/hrd80 ≈ 300 microns1,12 wt% of Cu
Tailings
W1 –Classification &
Grinding
W0 – ROM crushing & screening
W3 – Shale/ Carbonate
Beneficiation
Concentrate thickener
Tailings thickener
W2 –Sandstone
BeneficiationConcentrate
Recycled water
RoM
W1 – Classification & Grinding
W1 – Rod Mills
W1 – Ball Mill 1
W1 – Ball Mill 2
Water
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Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
CC - Climate change
PM - Particulate matter
RD - Resource depletion
WU - Water use
FE wLT - Freshwater ecotoxicity with long term
FE woLT - Freshwater ecotoxicity without long term
Impact Assessment for 1 ton of Cu in 13,7% Cu concentrate - Base Scenario
W0 - ROM crushing and screening W1 - Classification and grinding W2 - Sandstone benefication
W3 - Shale/Carbonate benefication W4 - Tailings thickeners W5 - Concentrate thickener
Case study: impact calculation
Direct emissions from tailings account for a
dominant part in freshwater ecotoxicity
on the long term
Resource depletion
here is exclusively
copper intake
Heavy contribution of W1, especially
electricity consumption
Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
COORDINATD BY
0
0,5
1
1,5
2
2,5
1995 2000 2005 2010 2015 2020
Cu
co
nte
nt
(wt%
)
Evolution of the Cu content in Lubin ore
Historical ore productiondata (1998-2011)
Mine Five Year ProductionPlan (2012-2016)
Worst case scenario
Scenario modelling
Historical trend
Scenario:Cu = 0.85wt%
Case study:Cu = 0.94wt%
Figures from MICON TECHNICAL REPORT ON THE COPPER-SILVER PRODUCTION OPERATIONS OF KGHM POLSKA MIEDŹ S.A. IN THE LEGNICA-GLOGÓW COPPER BELT AREA OF SOUTHWESTERN POLAND (Feb. 2013)
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Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
COORDINATD BY
0% 20% 40% 60% 80% 100% 120%
BS - 1 ton of Cu in 13,7% Cu Concentrate
WCS - 1 ton of Cu in 12,6% Cu concentrate
BS - 1 ton of Cu in 13,7% Cu Concentrate
WCS - 1 ton of Cu in 12,6% Cu concentrate
BS - 1 ton of Cu in 13,7% Cu Concentrate
WCS - 1 ton of Cu in 12,6% Cu concentrate
BS - 1 ton of Cu in 13,7% Cu Concentrate
WCS - 1 ton of Cu in 12,6% Cu concentrate
BS - 1 ton of Cu in 13,7% Cu Concentrate
WCS - 1 ton of Cu in 12,6% Cu concentrate
CC
PM
WU
FE w
LTFE
wo
LT
Impact Assessment - 1 ton of Cu in concentrate - Base Scenario (BS) and Worst Case Scenario (WCS)
W0 - ROM crushing and screening W1 - Classification and grinding W2 - Sandstone benefication
W3 - Shale/Carbonate benefication W4 - Tailings thickeners W5 - Concentrate thickener
Scenario: impact calculation
+13%
+12%
+12%
+11%
+10%
A RoM initially 9% poorer in copper requires >13% more energy to produce the same amount of copper concentrate. It also generates more emissions to air and more waste.
Most impacts rise by 10 to 13%.
Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
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Conclusions and outlook
Elaboration of a joint process and environmental simulation applied to the mineral industry:• Provides gains in robustness and time spent for mass balance’s
calculations• Direct link between process performance and environmental impacts• Highlights key unit operations to be improved/optimized on both
technical and environmental viewpoints
Applicability proven using “descriptive” process models in a prospective case study
Complementarity / coupling to be improved by:• Implementing a “hard” software connection between process simulation
software and LCA software• Using “predictive” process models in relation with equipment sizing and
upscaling data
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Coupling simulation of mineral processing with LCA| Bodin et al. | BRGM and DELOITTE
COORDINATD BY
THANK YOU FOR YOUR ATTENTION!ANY QUESTIONS?
www. .de
Study team (WP5 contribution):
BRGM: J. Bodin, J. Villeneuve, A. Beylot, K. Bru, F. BodénanDeloitte: A. Chanoine, P.A. Duvernois, C. Tromson, J. Bitar
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