DATA ANALYTICS—REAL VALUE BEING DELIVERED FOR THE MINING INDUSTRY

through Commercialization

Damien Duff VP Geoscience and Geotechnical R&D October 3rd, 2016 Mira Geoscience- Earth Modeling Forum

Centre for Excellence in Mining Innovation 129 Projects 28 Institutions

$33.2M Miners

$15.6M SMEs $6.6M Researchers

National & Global Reach Global recognition - Vale, Rio Tinto, Glencore Kidd, KGHM, LKAB, xxx Iamgold, Agnico Eagle, Newcrest, Glencore SINO, Shell, Nexen. © CEMI 2015

Centre for Excellence in Mining Innovation Established 2007, Not for Profit Corporation, Sudbury, Ontario

Vision To be the leading source of innovation for the global mining industry.

Mission To advance the innovation that the mining industry will use to:  find more ore,  mine ore more effectively and safely,  generate more value from mines,  have a more benign impact on the environment and,  a more beneficial impact on communities.

© CEMI 2015

Centre for Excellence in Mining Innovation 5 Strategic Pillars: Industry-focused Programs FINDMINE: Orebody Discovery Increase Ore Discovery Rate: Geophysics & Geochemistry DEEPMINE: Rock Stress & Heat Management Reduce Technical Risk VALUEMINE: Improve Mine Productivity Lean Mining, ROCE & ROI SUSTAINMINE: Environment & Sustainability Reduce environmental impact, Improve social license BIZMINE: Mining Business Program Support, Business Cases, Commercialization Services

© CEMI 2015

Canada’s Ultra-Deep Mining Network

TO HELP THE MINING INDUSTRY TO ADOPT COMMERCIALLY VIABLE R&D PROJECT RESULTS, AND ACCELERATE THE DEPLOYMENT OF PROVEN INNOVATIVE TECHNIQUES AND TECHNOLOGIES.

27 projects

45 network members

$35

million in private and public funds

Managed by CEMI

Addressing the Challenges of Ultra-Deep Mining

Managed by CEMI

“Data is the New Gold”!

~ 67% mining executive respondents say that big data AND analytics were having a significant and positive impact on their revenues (Absolutdata after Teradata-McKinsey survey)

CONTEXT • Collecting more data than we ever have (in the Tb range at times) • More installed sensors and more self-monitoring capability built into our mobile and fixed-plant equipment fleets • Opportunities for maintenance scheduling and associated cost reduction strategies • Opportunities for more awareness of process flows and their performance

Opportunities for “real-time” situational awareness and enhanced management decision support

Data Management/Analytics projects at CEMI 1. SUMIT― Smart Underground Monitoring and Integrated Technologies for deep mines 2. Energy audits at older mine sites 3. NHEA ― Natural Heat Exchange Areas Optimizing their effectiveness

4. Geothermal capacity of Mine water 5. GeoHazard Assessment in deep mines

SUMIT Smart Underground Monitoring and Integrated technologies for deep mines “Innovation leading to novel technologies and reduced investor risk” CEMI ANNUAL GENERAL MEETING (October 6th, 2016, Sudbury)

Data management in the SUMIT program

Step Change in Data Management Mira Geoscience

Geoscience INTEGRATOR: Initial Design Requirements

Data Management/Analytics at CEMI • SUMIT― Smart Underground Monitoring and Integrated Technologies for deep mines • Energy audits at older mine sites

Energy audit by [Data] analysis of a single mine electricity meter Michelle Y. Levesque Dean L. Millar Mining Innovation, Rehabilitation and Applied Research Corporation (MIRARCO) & Bharti School of Engineering, Laurentian University, Sudbury, ON April 15, 2015

www.mirarco.org

You can’t manage what you can’t measure … but how can we measure component electricity use at mine sites without submeters? FOURIER ANALYSIS: the analysis of a complex waveform expressed as a series of sinusoidal functions, the frequencies of which form a harmonic series.

www.mirarco.org

Signals can be represented in the time or frequency domains Signal 1: Frequency = 10 , Amplitude = 1

Single-Sided Amplitude Spectrum of Signal 1

FFT

1 0.8

1

0.6 0.8

0.2 Mag

Amplitude

0.4

0

0.6

-0.2 -0.4

0.4

-0.6

IFFT

-0.8

0.2

-1 0

0.2

0.6 0.4 Time (seconds)

0.8

1

0 0

20

60 40 Frequency (Hz)

80

100

Even complicated signals can be represented in time or frequency domains Signal 1: Frequency = 10 , Amplitude = 1

Single-Sided Amplitude Spectrum of Signal 1 1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 Time (seconds)

0.8

1

0.5 0 0

20

40 60 Frequency (Hz)

80

100

Even complicated signals can be represented in time or frequency domains Signal 1: Frequency = 10 , Amplitude = 1

Single-Sided Amplitude Spectrum of Signal 1 1 Mag

Amplitude

2 0 -2 0

0.8 0.6 0.4 Time (seconds) Signal 2: Frequency = 2 , Amplitude = 0.7 0.2

0.5 0 0

1

80 60 40 Frequency (Hz) Single-Sided Amplitude Spectrum of Signal 2 20

100

1 Mag

Amplitude

2 0 -2 0

0.2

0.6 0.4 Time (seconds)

0.8

1

0.5 0 0

20

60 40 Frequency (Hz)

80

100

Even complicated signals can be represented in the time or frequency domains Signal 1: Frequency = 10 , Amplitude = 1

Single-Sided Amplitude Spectrum of Signal 1 1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 0.8 Time (seconds) Signal 2: Frequency = 2 , Amplitude = 0.7

0.5 0 0

1

20

40 60 80 Frequency (Hz) Single-Sided Amplitude Spectrum of Signal 2

100

1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 Time (seconds) Combined signal

0.8

0.5 0 0

1

20

40 60 80 100 Frequency (Hz) Single-Sided Amplitude Spectrum of Combined signal

1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 Time (seconds)

0.8

1

0.5 0 0

20

40 60 Frequency (Hz)

80

100

How do we extract the individual signals from the combined signal? Signal 1: Frequency = 10 , Amplitude = 1 Amplitude

2 0 -2 0

0.2

0.4 0.6 0.8 Time (seconds) Signal 2: Frequency = 2 , Amplitude = 0.7

1

Amplitude

2 0 -2 0

0.2

0.4 0.6 Time (seconds) Combined signal

0.8

1

0.2

0.4 0.6 Time (seconds)

0.8

1

Amplitude

2 0 -2 0

Separating the signals can be done in the frequency domain

Combined signal Amplitude

2 0 -2 0

0.2

0.6 0.4 Time (seconds)

0.8

1

Separating the signals can be done in the frequency domain

Combined signal

Single-Sided Amplitude Spectrum of Combined signal 1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 Time (seconds)

0.8

1

0.5 0 0

20

60 40 Frequency (Hz)

80

100

Separating the signals can be done in the frequency domain

Combined signal

Single-Sided Amplitude Spectrum of Combined signal 1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 Time (seconds)

0.8

1

0.5 0 0

20

60 40 Frequency (Hz)

80

100

Separating the signals can be done in the frequency domain

Single-Sided Amplitude Spectrum of Signal 2

Signal 2: Frequency = 2 , Amplitude = 0.7 1 Mag

Amplitude

2 0 -2 0

0.2

0.6 0.4 Time (seconds) Combined signal

0.8

0.5 0 0

1

100 80 60 40 Frequency (Hz) Single-Sided Amplitude Spectrum of Combined signal 20

1 Mag

Amplitude

2 0 -2 0

0.2

0.6 0.4 Time (seconds)

0.8

1

0.5 0 0

20

60 40 Frequency (Hz)

80

100

Separating the signals can be done in the frequency domain

Single-Sided Amplitude Spectrum of Signal 2

Signal 2: Frequency = 2 , Amplitude = 0.7 1 Mag

Amplitude

2 0 -2 0

0.2

0.6 0.4 Time (seconds) Combined signal

0.8

0.5 0 0

1

100 80 60 40 Frequency (Hz) Single-Sided Amplitude Spectrum of Combined signal 20

1 Mag

Amplitude

2 0 -2 0

0.2

0.6 0.4 Time (seconds)

0.8

1

0.5 0 0

20

60 40 Frequency (Hz)

80

100

Separating the signals can be done in the frequency domain Signal 1: Frequency = 10 , Amplitude = 1

Single-Sided Amplitude Spectrum of Signal 1 1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 0.8 Time (seconds) Signal 2: Frequency = 2 , Amplitude = 0.7

0.5 0 0

1

20

40 60 80 Frequency (Hz) Single-Sided Amplitude Spectrum of Signal 2

100

1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 Time (seconds) Combined signal

0.8

0.5 0 0

1

20

40 60 80 100 Frequency (Hz) Single-Sided Amplitude Spectrum of Combined signal

1 Mag

Amplitude

2 0 -2 0

0.2

0.4 0.6 Time (seconds)

0.8

1

0.5 0 0

20

40 60 Frequency (Hz)

80

100

How can this be applied to an underground mine? 1.2 1.1

Normalized electricity

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Jan 19

Jan 20

Jan 21

Jan 22

Jan 23

Jan 24

Jan 25

Jan 26

A pattern resembling that of a mine hoist emerges if we look closer Normalized energy

1.5 1 0.5 0 -0.5 0

5

10

20

15 Time (minutes)

25

30

35

A pattern resembling that of a mine hoist emerges if we look closer Normalized energy

1.5 1 0.5 0 -0.5 0

5

10

15

20

25

30

35

20

25

30

35

Normalized energy

Time (minutes)

1 0.5 0 -0.5 0

5

10

15 Time (minutes)

A pattern resembling that of a mine hoist emerges if we look closer Normalized energy

1.5 1 0.5 0 -0.5 0

5

10

15

20

25

30

35

20

25

30

35

Normalized energy

Time (minutes)

1 0.5 0 -0.5 0

5

10

15 Time (minutes)

We can get the fingerprint of the mine hoist to know which frequencies to filter Norm. magnitude

Norm. energy

1.5 1 0.5 0 -0.5 0

5

10

15 20 Time (minutes)

25

30

35 Norm. magnitude

Norm. energy

1.5 1 0.5 0 -0.5 0

5

10

15 20 Time (minutes)

25

30

35 Norm. magnitude

Norm. energy

1.5 1 0.5 0 -0.5 0

5

10

15 20 Time (minutes)

25

30

35

1.5 1

actual signal synthesized signal

0.5 0 0

0.2 0.4 0.6 0.8 Norm. frequency (π rads/sample)

1

1.5 1

actual signal synthesized signal

0.5 0 0

0.2 0.4 0.8 0.6 Norm. frequency (π rads/sample)

1

1.5 1

actual signal synthesized signal

0.5 0 0

0.2 0.4 0.6 0.8 Norm. frequency (π rads/sample)

1

We can train a Neural Network to recognize periods when the hoist is operating 1

0.1

0.8

0.05

0.6 0

5

10

25

30

0 0

35

0.1

0.8

0.2 0.4 0.6 0.8 Normalised frequency (π rads/sample)

1

0.2 0.4 0.6 0.8 Normalised frequency (π rads/sample)

1

0.2 0.4 0.6 0.8 Normalised frequency (π rads/sample)

1

0.2 0.4 0.6 0.8 Normalised frequency (π rads/sample)

1

0.05

0.6 0

5

10

1

15 20 Time (minutes)

25

30

35

0.8 0.6 0

5

10

1

15 20 Time (minutes)

25

30

35

0.8

Normalised magnitude

Normalised electricity (kWh)

1

15 20 Time (minutes)

0 0 0.1 0.05 0 0 0.1 0.05

0.6 0

5

10

15 20 Time (minutes)

25

30

35

0 0

Now we know when the hoist was operating and what frequencies to filter

0.5

0 1

0.75

0.5

0.25 Jan 19

Jan 20

Jan 21

Jan 22

Jan 23

Jan 24

Jan 25

Jan 26

Probabilty of hoist running

Normalized total electricity

1

The hoist consumed 4% of the total electricity at the mine 0.75

Normalized hoist electricity

0.5

0.5

0 0.25

0

Jan 19

Jan 20

Jan 21

Jan 22

Jan 23

Jan 24

Jan 25

Jan 26

Probabilty of hoist running

1

We can repeat the process for different equipment with the filtered signal 1.2 1.1

Normalized electricity (kWh)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Jan 19

Jan 20

Jan 21

Jan 22

Jan 23

Jan 24

Jan 25

Jan 26

Such as: ventilation, pumps, compressors… until all electricity is characterized 1.2 1.1

Normalized electricity (kWh)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Jan 19

Jan 20

Jan 21

Jan 22

Jan 23

HUGE OPPORTUNITY FOR SAVINGS!

Jan 24

Jan 25

Jan 26

Data Management/Analytics at CEMI • SUMIT― Smart Underground Monitoring and Integrated Technologies for deep mines • Energy audits at older mine sites • NHEA ― Natural Heat Exchange Areas • Optimizing their effectiveness

environmental sensor door

model

state

17MWth 3MWth

An analytical model [left] reflects, to some degree, NHEAthe currently flow through displacing the 17 broken rock and the physics of heat transfer. MW refrigeration plant. A computational fluid dynamics model [right] gives Additional higher 3 MW fidelity displaced on the airflow paths and the physics. with decision support Graphics: Courtesy MIRARCO

Managed by CEMI

Data Management/Analytics at CEMI • SUMIT― Smart Underground Monitoring and Integrated Technologies for deep mines • Energy audits at older mine sites • NHEA ― Natural Heat Exchange Areas • Optimizing their effectiveness • Geothermal Capacity of Mine water

Managed by CEMI

The Solution- 2 Geothermal Resources from Open Loop Systems • Few studies on the geothermic potential of waste mine water • Desirable ground temperature • Abundant sources of underground water • Extensive underground excavations: No drilling required • Best Case study (Spring Hill) done after mine closed • Unanswered questions • Performance assessment unclear

Managed by CEMI

Geothermal Mine Water Database

Research Objectives • Practical determination of the performance of an open loop geothermal system installed in a working Canadian mine • Determination of the acceptable geothermal energy resource obtainable from abandoned and working mines in Canada • Identification of the optimum technique for using this geothermal energy in mining infrastructure and/or mineral processing operations • …

Managed by CEMI

Geothermal Mine Water Assessment •

Analyze the design aspects of open loop systems in Canadian Mines Develop computer simulation model to verify theories and computations Water

Mine Operation

Flow

(GPM)

Mine 1

Water Temperature (0C)

Heat

Gain

Annual

CO2

Savings

Reduction

($/yr)

(t/yr)

(kW)

N.G.

E

N.G.

E

1000

16.5

1,160

-

305,360

1427

252

375

16.7

430

-

115,023

537

95

320

22

430

2,805

113,772

513

91

398

16.7

465

125,930

188,092

554

305

950

20.9

1,578

167,173

415,116

1,897

2,692

290

18.9

360

21,000

205,000

437

620

133

16.6

160

8,000

88,000

190

270

515

16.7

600

31,000

342,000

738

1,048

675

14

730

30,000

414,000

913

1,296

560

15.8

640

30,500

362,000

787

1,116

Mine 11

800

11.5

800

24,500

455,540

1,025

1,455

Mine 12

719

13.8

760

288,000

136,000

1,102

209

Mine 13

116

13.6

125

46,220

21,800

177

34

Mine 2 Mine 3

•

Mine 4 Mine 5 Mine 6 Mine 7 Mine 8 Mine 9

Mine 10

Calculations demonstrate that economic savings, and sometimes significant ones, are possible at all operations visited, were the geothermal capacity of the mine water to be captured and utilized for heating purposes. Managed by CEMI

Data Management/Analytics at CEMI • SUMIT― Smart Underground Monitoring and Integrated Technologies for deep mines • Energy audits at older mine sites • NHEA ― Natural Heat Exchange Areas • Optimizing their effectiveness • Geothermal Capacity of Mine water • GeoHazard Assessment in deep mines

Managed by CEMI

Geohazard hazard (x, y, z, t) = f (rock quality, geometry, stress, seismicity, geology, …)

courtesy Xstrata Zinc BMS#12 Mine

Multi-Disciplinary Integration: Mining January 10, 2000 January 16, 2001 November 15, 2001 July 18, 2002 September 1, 2003 November 26, 2003 July 15, 2004 May 27, 2005 June 1, 2005 April 9, 2006 April 21 2006 May 21, 2006 June 20, 2006 November 29, 2006 March 19, 2008

©2014 Mira Geoscience Ltd.

Evaluating the Results Craig Mine Ore Zone, 10 April 2008

“Efficiency of Classification” Curve

Percentage of Rockbursts

100 80 60

80% of historical fault-slip rockbursts occur at a computed hazard index of 90% or greater

40 20 0 100

80

60

40

Hazard Score

20

0

Evaluating the Results Creighton Footwall, 14 March 2009

“Efficiency of Classification” Curve

100 80 60

80% of historical fault-slip rockbursts occur at a computed hazard index of 75% or greater

40 20 0 100

80

60

40

20

0

Predictive Analytics

weights-of-evidence can be considered an optimum assessment of how well historical hazard statistically correlates to observation: a quantitative characterization of “typical” behaviour predictive analytics characterizes both “typical” behaviour and “outlier” behaviour, finding specific combinations of observations that correlate to historical hazard

MODCC OVERVIEW

MINING OBSERVATORY DATA CONTROL CENTRE

Dec 12th 2014

The space—

Revolution Mining

Mira Geoscience

Tunik Inc.

CONCLUSIONS

Value/Impact: • Decision support (real-time or not) • Cost savings • Design Optimization MODCC: • growth of data analytics for mining fostered by enhancing mining capability and capacity Managed by CEMI

CONCLUSIONS

“Data is gold but data analytics (and MODCC) is platinum”! Thanks! Email: [email protected]

Managed by CEMI