The role of robotic innovation in ore characterization, mineralogy and geometallurgy - Mette Dobel, FLSmidth

The information contained or referenced in this presentation is confidential and proprietary to FLSmidth and is protected by copyright or trade secret laws.

The role of robotic innovation in ore characterization, mineralogy and geometallurgy

Mette Dobel, Global Product Manager, FLSmidth Co-author – Kevin Ausburn, Chief Mineralogist, FLSmidth

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Safety Share

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Where is the technology currently when considering industrial automation

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Mature: Ore grade

Innovative: Mineralogy

Future: Geometallurgy


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Mature technology: Chemical analysis of exploration and mine site materials

Example of such ore grade automated laboratory Coarse preparation and fire assay dosing


Labtium Oy, Rovaniemi

5 15 September 2013 QCXM Sales Presentation

Sample weighing

Sub-sample dosing and

tagging

Dosing of FA flux

Mixing Filling in FA

crucible

Crushing Splitting Reject bagging

and tagging Pulverizing Bowl cleaning


Innovative Technology: Mineralogical analysis

Different methodologies are emerging from the R&D laboratories through automation efforts. I will focus on two:

Automated sample preparation integrated with

X-ray diffraction

using the Rietveld Method

Automatic logging of drill cores using

hyper spectral technology

interfacing to the CSIROs material database.

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Robotized Sample Prep – for XRD analysis

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Crushing Splitting Pulverizing Bagging and

tagging Fine milling Splitting Soft press XRD


Dosing of powder sample

Sample leveling / cleaning of

dosing section

Sample pressing/

cleaning of leveller

Moving of ring with pressed

material to output position / cleaning of pressing section

Cleaning of ring stored for the longest time in

magazine

Moving of ring to input position / storage of used ring in magazine

ASP 100 is ready for another pressing

The innovation: ASP100

Sample pouring

Sample holder with

pressed sample


Performance evaluation

The XRD prep and analysis system was installed to:

Reduce turnaround time (from sample to analysis)

Improve preparation and analytical performance

Cost benefit

Safety improvement

Experiments and analysis have been made to evaluate these targets against the excellent manual laboratory procedures currently in place

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Performance target Outcome

Reduce turnaround time

Improve analysis

Cost benefit

Safety improvement


Turnaround time

Method: 8 sample batch from dried coarse sample to XRD analysis. Timed the duration of the process in the manual and automated process

Results:

Manual = 2 hours 29 minutes

Automation = 1 hour 31 minutes

39% reduction in processing time

For the daily capacity of 200 Samples =

56% reduction in processing time

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Cross Contamination test in progress

Method: Process sequentially Quartz samples and Copper Ore samples: Q1 Cu1 Q2 Cu2 Cu3 Cu4 Cu5 Q3.

Run this sequence in both the RoboLab and in manual equipment

Results: Robotized system cross contamination = 0.47% distribution of material carryover Manual preparation cross contamination = 0.49% distribution of material carryover

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-0.300

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-0.050

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0.050

0.100

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Dis

pla

cem

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m)

Test #

Sample Displacement

RoboLab Pressed Sample

Manual Back Loaded Sample

Average = 0.05STDEV = 0.02

Average = -0.18STDEV = 0.04

Significantly

•less preferred orientation

•less sample displacement

•less variance

=

•Improved sample analysis quality

•Optimized process design

•Optimized production process

A complex research based technique is suddenly available as an industrial process

-150 -100 -50 0 50 100 150Phi (°)

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nsity (

counts

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-150 -100 -50 0 50 100 150Phi (°)

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nsity (

counts

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Quality Benefits in XRD analysis

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ASP100

Manual backloading


Financial evaluation

Input:

Delta of investment between manual and automated capital cost is $1.1 Million USD

Personnel reduction of 4

Increase in lab productivity due to max. utilization of equipment and faster through put

Cost of capital of 10%

Result:

Payback in year 5

NPV at year 15 = $1.6 Million USD

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-1.500.000

-1.000.000

-500.000

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500.000

1.000.000

1.500.000

2.000.000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


Safety improvements

Many repetitive and hazardous tasks eliminated through the preparation process

Hard to measure, but examples include the dust inhalation, sample lifting, noise, injury from equipment

FLSmidth strives for 4 LTI hours per 1 million hours.

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Conclusions

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Performance target Outcome

Reduce turnaround time Less then half

Improve analysis Automated superior to manual

Cost benefit NPV of $1.5M USD

Safety improvement Hazards reduced


Another innovation: Hyperspectral mineralogical logging and imaging of drill core and chips

9/15/2013 Title 16

HyLoggingTM: a new set of eyes to rapidly and objectively quantify minerals


Fundamentals of HyLogging spectrometry

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HyLogging hardware

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2 reflectance spectrometers (VNIR / SWIR ) 400-2500nm

Calibration targets and internal references

Digital camera (linescan or framing)

Lighting (two sets – camera and spectrometers)

..& software

‘The Spectral Geologist’ sw data treatment

Images

Historic data available for later evaluation


HyLogging advantages Rapid collection of high density spectral reflectance measurements.

• Continuous high-resolution color imagery

• Objective, consistent and effective core logging

• Increased geological knowledge

• Near real time analyses

• Non destructive

• Samples measured in their original trays

• Maximizes the value of drilling expenditure

• Improved geometallurgical understanding

• Historical digital archive of all cores and rock chips

• Addresses shortage of skill and expertise


The future could be…

Automated geometallurgy as done by scanning electron microscopes combined with automated sample preparation and high level software direct optimization of the minerals processing plants.

..to make R&D based techniques available for production in mines and minerals processing plants through reliable

economically justifiable solutions

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Thank you!

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The role of robotic innovation in ore characterization, mineralogy and geometallurgy - Mette Dobel, FLSmidth

Economy & Finance

trade secret laws

dosing of powder sample

sample batch

sample holder

asp100 sample

automated sample preparation

qcxm sales presentation

subsample dosing