Next generation automation for the mining industry enabled by industry4.0 4IR technologies. The global C4IR(TM) event series is available to franchise via www.cir-strategy.com as C4IRx.

1. Next Generation Automation for the Mining
Industry enabled by Industry4.0 technologies
Sam G. Bose
Founder & CEO
presented to:
presented by:
C4IR: Executive Master Class
Presented at Consortium for the 4th Revolution | Executive Briefing Day
(#C4IR) Cambridge, UK 2-3 February 2017 | www.cir-strategy.com/events

2. Agenda
• About IntelliSense.io
• Industry 4.0 Technologies and it’s relevance for Mining Industry now?
• Case Study: Copper Mining in Chile
• Questions and Answers
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3. IntelliSense.io: Empowering People & Machines to make better decisions
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Academic Alliance:
Expertise:
Internet of Things, Sensor Data Analytics & Decision Support
Natural Resources Industry (Mining, Oil & Gas)
Founder & CEO:
Sam G. Bose
HQ: Cambridge UK
Operations
Dev Center
Operations

4. IntelliSense.io: Our Customers
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Key Partners
Chile / Latin America Kazakhstan / Central Asia

5. What is Internet of Things?
Transformation of any physical object to digital data object by attaching sensors and communications to it.
Computing Era
Transformation
2016 +
• 6.4 billion internet connected things in 2016, up 30% from 2015 (Source: Gartner)
• More Data created in the past 2 years than entire history of human race, but less than 0.5% of all data is ever
analysed (Source: Fortune)
• Industry-wide (Oil & Gas, Mining) adoption of IoT technology could increase global GDP by as much as 1.2
percent, or $930 billion during the next decade (Source: Oxford Economics, McKinsey)
IoT Today
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6. What is Industry 4.0?
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Industrial Internet of Things
INDUSTRY
1st REVOLUTION 2nd REVOLUTION 3rd REVOLUTION 4th REVOLUTION
Water/Steam Electricity Automation Intelligence Era

7. Applying Internet of Things & Artificial Intelligence technologies in Mining
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The “things”
mining
operations
pit-to-port
Instruments
and sensors
Integrated data
platform, analysis and
data models
Connectivity,
communications
and controllers
Decision support tools for
prediction, optimization and
simulation
Continuous
Optimisation

8. Why should the Mining Industry adopt new technologies?
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Since 2009
US coal
companies have
gone into
bankruptcy &
mines closed.
26
264
Capex fell to just
in 2015 – half of
the levels seen in
2012 & 2013.
Top 40 Mining
companies suffered
their first collective net
loss in history of
record high leverage of
46% and operating
expenditure cuts of
$27b
$83b
$69b
Source: PwC Mine 2016; Moody’s
Corporate Default & Recovery Rates
Highest default rate in 2015 ever,
for Mining & Metals companies,
with Oil & Gas companies at
6.5%
6.3%

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Why should the Mining Industry adopt new technologies?
Declining mining productivity fuels investment in “transformative” technology

10. Next Generation Automation: Integrated Pit-to-Port Operation
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11. Confidential | © 2017 1111
Material Transport Model
Accurately tracks the mass flow and
properties through a system.
Material Influence Model
Accurately predicts how the geometallurgical and
physical properties of the feed material affect system
performance.
CRUSHING GRINDING LEACHMINE
STOCK PILE
GPS
THICKENER/CCDFLOTATION
THICKENER
TRANSPORT
Cu CONCENTRATE
EW Cu CathodeSTOCK PILING & WASTE
h
GPS
WASTE
GPS
SEAWATER TAILINGS
SX
THICKENER
Industry Innovation: Material Model: Tracking & Predicting Material Flows

12. Industry Innovation: Prediction Based Controls & Optimisation
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Run a digital plant model based
on custom set of control
variables.
simulate
Powerful Training Tool
Operational Configuration Tool
Future: Operational and
Financial KPIs
Predict
Decision-making Tool
Prediction-based Alerts
Optimised Operational and
Financial KPIs
optimise
Control Variables
Continuous
Recommendations/Set Points
Automatically fed to the PLC.
Root Cause Analysis
Real Time
The user can run a digital plant
model based on custom set of
control variables.
Simulate
Powerful Training Tool
Operational Configuration Tool
Optimised Operational and
Financial KPIs
Optimise
Control Variable
Recommendations/Set Points
that are automatically fed back
into the PLC.
Root Cause Analysis

13. Hybrid Cloud Architecture
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USP
• Hybrid Cloud: Ability to support on
premise high availability and low
latency control situations
• Real time stream parsing of
physical and machine learning
model outputs
• Horizontal scalability

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Accurate Physical and Geometallurgical PropertiesUnknown Material Properties
System View KPI VisualisationLimited Data Visualisation
Virtual SensorsExpensive Sensor CAPEX
System Wide Dynamic Predicted Set pointSet Points Decided by Engineer/Process
Owner
One Single Data LakeManual Multi Source Data Gathering
Old World: New World:
Syste
m
Old World Emerging New World
Next Generation Automation: How is it different from today?

15. User Case – Largest Copper Market in the world (Chile) & Mining Challenges
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Water Scarcity High Energy Cost
Declining Ore Grade Low Commodity Price

16. Case Study: Thickener Circuit Optimisation
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Benefits*:
• Increase underflow % solids
• Enhanced water recovery up to 10%
• Reduced flocculent consumption up to 5%
• Payback period: within 6 months
Typical Challenges
• Increased feed mineralogy variability
• Low underflow % solids & water
recovery
• High flocculent consumption
IntelliSense.io Technologies
• Accurately predict feed mineralogy -- geo-metallurgical & physical properties
• Provide optimal set point recommendations to the expert system
• Deploy optimisation simulator for diagnostic & training
* Actual values are client confidential data
Minera Centinela Characteristics
• 105,000 ton of Copper processed per day
• Copper and Gold Mine, one of the largest
in Latin America

17. Thickener Circuit Optimisation & Stability: Business Case
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Reduce Underflow % Solid Variance Impact of Reduced Variance + 0.6 % Solid
Note: Benefits analysis performed on thickener circuit historical data from June to August 2015
Enhanced Water Recovery

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User Types and Scenarios
Tailings / Process Control Engineer Thickener Operator Plant Manager
• Real time visibility of circuit
performance for root cause analysis
• Calibrate circuit optimisation rules
• Access to simple to use simulator for
delivering training to the operators
• Real time predicted geo-metallurgical
and physical properties of the feed
material for decision making
• Executing Prediction based alerts (1
hour)
• Tracking the impact of the changes
to performance variable set points
• Real time visibility of circuit
performance financial perspective ($/
tonne processed)
• Access to Executive Level Benefit
Tracking for Circuit Optimisation and
ad hoc performance reports
• Simulating the impact of changes to
circuit design based on real time
performance simulator
brains.app for Thickener Circuit : user scenarios

19. brains.app for Thickener Circuit: Tailings and Process Control Engineer
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Circuit Optimisation CalibrationReal Time Debottlenecking Dashboards Training Simulator
Input: Feed &
Control Variables
Output: Thickener
Performance (+1 hour)

20. brains.app for Thickener Circuit: Thickener Operator
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Prediction Based AlertsPredicted Geo-Metallurgical Properties Optimised Control Variable Set Points
Historical
Real Time
Future Performance
(+ 1 hour)
Alert: Predicted Underperformance
Recommend: Control
Variable Set Points
Optimise: Improve Underflow % Solids

21. brains.app for Thickener Circuit: Plant Management
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Executive Level Economic DashboardReal Time Financial Performance Circuit Design Change Simulator
Real time visibility of circuit performance
($ / tonne processed)

22. Questions and Answers
Thank you
Sam G. Bose
CEO & Founder, IntelliSense.io
sam@IntelliSense.io
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