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

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

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 Confidencial | © 2017

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 Confidencial | © 2017

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 Confidencial | © 2017

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 Confidencial | © 2017

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

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

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. Confidencial | © 2017 9

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

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 Q3 2017. 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

Contacts Us Joanne Hurley Global Account Director joanne@IntelliSense.io Confidencial | © 2017