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
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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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
IntelliSense.io: Our Customers
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Key Partners
Chile / Latin America Kazakhstan / Central Asia
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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What is Industry 4.0?
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Industrial Internet of Things IND
US
TRY
1st REVOLUTION 2nd REVOLUTION 3rd REVOLUTION 4th REVOLUTION
Water/Steam Electricity Automation Intelligence Era
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
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
Next Generation Automation: Integrated Pit-to-Port Operation
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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
STOCKPILEGPS
THICKENER/CCDFLOTATION
THICKENER
TRANSPORT
CuCONCENTRATE
EW CuCathodeSTOCKPILING&WASTE
h
GPS
WASTE
GPS
SEAWATER TAILINGS
SX
THICKENER
Industry Innovation: Material Model: Tracking & Predicting Material Flows
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
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 Properties Unknown Material Properties
System View KPI Visualisation Limited Data Visualisation
Virtual Sensors Expensive Sensor CAPEX
System Wide Dynamic Predicted Set point Set Points Decided by Engineer/Process Owner
One Single Data Lake Manual Multi Source Data Gathering
Old World: New World:
System
Old World Emerging New World
Next Generation Automation: How is it different from today?
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
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
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
brains.app for Thickener Circuit: Tailings and Process Control Engineer
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Circuit Optimisation Calibration Real Time Debottlenecking Dashboards Training Simulator
Input: Feed & Control Variables
Output: Thickener Performance (+1 hour)
brains.app for Thickener Circuit: Thickener Operator
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Prediction Based Alerts Predicted 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
brains.app for Thickener Circuit: Plant Management
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Executive Level Economic Dashboard Real Time Financial Performance Circuit Design Change Simulator
Real time visibility of circuit performance ($ / tonne processed)
Questions and Answers
Thank you
Sam G. Bose CEO & Founder, IntelliSense.io
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