1. Empowering People & Machines to Make Superior and Reliable Decisions
IntelliSense.io Mining Overview
Empowering People & Machines to Make Superior and Reliable Decisions
May 2017

2. IntelliSense.io: Company Overview
Our Partners
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
2017 Enterprise Awards Finalist Company​​
Confidencial | © 2017

3. The “things” mining operations pit-to-port
Value Proposition: Applying Internet of Things (IoT) & Artificial Intelligence (AI) to the Mining Industry
Decision support tools for prediction, optimisation and simulation
Continuous optimisation
Integrated data platform, analysis and data models
Connectivity, communications and controllers
Instruments and sensors
The “things” mining operations pit-to-port
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4. IntelliSense.io Differentiators: Combining Sensors, Software and Simulation to Deliver Optimisation for the Mining Industry
Artificial Intelligence & Physical Models
in Process Control Systems
No CAPEX Business Model
Live Sites
Deep Learning & Neural Networks
Physical Equipment and Process Models
Virtual Sensors
Continuous Optimisation
Annual Subscription
Quarterly upgrades
Thickener Optimisation
Grinding Optimisation
Pipeline Pumping Optimisation
In-Situ Leaching
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5. Next Generation Automation: Integrated Pit-to-Port Operation
Applications: Live
Pipeline Optimisation
Grinding Circuit Optimisation
Thickener Circuit Optimisation
In-Situ Leaching Optimisation
Pumping System Optimisation
Applications: In Development
Tailings Dam Monitoring and Optimisation
Material Composition Tracking
Floatation Circuit Optimisation
Mine Planning and Reconciliation
Furnace Optimisation
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6. Hybrid Cloud Architecture: Resilient & Scalable
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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7. Next Generation Automation:
How is it Different From Today?
Old World:
New World:
Unknown Material Properties
Accurate Physical and Geometallurgical Properties
Expensive Sensor CAPEX
Virtual Sensors
Manual Multi Source Data Gathering
One Single Data Lake
Limited Data Visualisation
System View KPI Visualisation
Set Points Decided by Engineer/Process Owner
System Wide Dynamic Predicted Set point
System
Old World
Emerging
New World
Confidencial | © 2017

8. Industry Innovation: Prediction Based Controls & Optimisation
Real Time
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
Simulate
Optimise
The user can run a digital plant model based on custom set of control variables.
Control Variable Recommendations/Set Points that are automatically fed back into the PLC.
Optimised Operational and Financial KPIs
Powerful Training Tool
Operational Configuration Tool
Root Cause Analysis
Confidencial | © 2017

9. Industry Innovation: Tracking & Predicting Material Flows (Material Balance)
Material Influence Model
Accurately predicts how the geometallurgical and physical properties of the feed material affect system performance.
Material Transport Model
Accurately tracks the mass flow and properties through a system.
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10. Case Study: Thickener Circuit Optimisation
Typical Challenges
Increased feed mineralogy variability
Low underflow % solids & water recovery
High flocculent consumption
Major Copper Mine - Chile
Over 100k tonnes of Copper processed per day
Copper and Gold Mine, one of the largest in Latin America
IntelliSense.io Technologies
Accurately predict feed mineralogy -- geo-metallurgical & physical properties
Deliver optimised control variable set points to the Expert System
Deploy optimisation simulator for diagnostics & training
Benefits*:
Increased underflow % solids by 1.5%
Reduced Variability by 55%
Payback period: within 6 months
* Actual values are client confidential data
Confidential | © 2017

11. Case Study: SAG Mill Optimisation
Major Copper Mine - Chile
Among the largest SAG mills in the world.
The SAG mill constitutes nearly 80% of the mine’s total consumption.
Typical Challenges
Ore hardness increases accelerate liner and grinding media wear rates and increase energy consumption.
Bearing weight overloads cause throughput reductions
IntelliSense.io Technologies
Deep neural network performance predictions under a wide range of operating conditions.
Combined AI and physical modelling approach for model robustness.
Control set-point recommendations increase throughput while optimizing specific energy consumption.
This 36 minute slowdown due to bearing pressure overload lowered throughput by 813 tonnes of ore with a copper value of $18,000.
Project Status and Next Steps
User acceptance testing completed.
Commissioning by Q
Benefits Targeted:
Delivery of improved throughput between 0.5% to 1%
Payback period within 6 months
Deep neural network performance predictions track future measurements
Confidential | © 2017

12. Contacts Us Joanne Hurley Global Account Director
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