Rethinking digital - presentation by Jani Puroranta 22.5.2018

1. © Metso
Jani Puroranta
Chief Digital Officer Metso Corporation
Mines and Technology Europe
May 30, 2018
How to use AI and IoT
to optimize comminution circuit
uptime and performance

2. © Metso
Three key levers to process improvement in minerals
processing and potential new digital solutions
Pilot case study: Cloud-based IoT solution for predictive
maintenance of cone crushers in an African mine
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How to approach improving crushing circuit availability
and performance with cloud-based technologies and AI
In this presentation
you will hear about...
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3. © Metso
Sources of lost production
in minerals processing
Productiontonnage
manual
operation
planned
shutdown
process
drift
emergency
shutdown
upset
conditions
good
control
Actual tonnage
Lost production
potential
Time
set point
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4. © Metso
Three key levers
to process improvement
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1. Stabilize
Reduce process variability
2. Optimize
Increase production rate
3. Maximize
Asset availability & uptime

5. © Metso
How to approach
predictive maintenance of the crushing circuit
1. Understand
the problem
2. Identify
root causes
3. Detect
operational
anomalies
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6. © Metso
Mean-time-to-repair(MTTR)
Incident frequency
Cracked mainframe
Burned bushings
Primary gyratory
Screens
Other
Damaged shaft
Cone crushers
Low lube pressure
High lube temp
Damaged head
Spider bushing damage
Conveyor belt damaged
Damaged
chute
Missing/broken mesh
Broken
mechanism
What are the actual
root causes?
More data is needed.
Jack-knife analysis
1. Understand
the problem
How to approach
predictive maintenance of the crushing circuit
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7. © Metso
Fishbone diagram
2. Identify
root causes
Environment Material People
Machine Method Measure
Burned
bushings
Ignoring
machine alarms
Wet
ore
Packing
condition
Maximize
shift tonnage
No good measurement
to indicate special
process condition
Changing ore
properties
How to approach
predictive maintenance of the crushing circuit
Could we develop
an algorithm to detect
and even predict the
development of packing
condition in real-time?
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8. © Metso
P-F curve concept
3. Detect
operational
anomalies
Failure Agent
Early stage
Potential Failure
Anomaly Agent
P
Functional
failure
P
Late stage
Potential Failure
F
Monitors already
known patterns and
early indicators of failure
Identifies a steady-state
operating pattern and
looks for deviation
F A
How to approach
predictive maintenance of the crushing circuit
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9. © Metso
Pilot case study
Metso Nordberg® MP cone crushers in African mine
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Three cone
crushers
connected
to the cloud

10. © Metso
Pilot case study
Metso Nordberg® MP cone crushers in African mine
Understanding crusher data
Co-operation is key
Data engineers
Mechanical engineers
Process engineers
Reliability engineers
Control engineers
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11. © Metso
Failure
Detected a leakage in the hydraulic
system a week in advance
Trained to detect early damage to
crusher head based on an actual failure
Anomaly Agent Failure Agent
Two types
of predictive
agents
deployed
FA
Pilot case study
Metso Nordberg® MP cone crushers in African mine
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12. © Metso CIM 2018 | Jani Puroranta | Metso
Analytics
Process
engineering
Condition
monitoring
Predictive
maintenance
OEM support
Root cause analysis
Analytics, big data and AI
in crusher optimization
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13. © Metso
Tons of
sellable product
Current
tonnage
Benefits
Cloud-based technologies and AI in comminution
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Elimination of
unplanned downtime
prevented downtime events
less severe downtime events
planned (vs unplanned) interventions
Optimization of circuit
and operating
parameters
% sellable product
% target size
recovery rate
Shorter, safer and less
frequent planned
shutdowns
more regular hours (i.e. less overtime)
availability of right people
availability of right parts

14. © Metso
A sneak preview
into the future
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15. © Metso
Maximizing equipment
availability and uptime
with cloud-based
technologies and AI.
Combining big
data analytics and
engineering expertise
to understand complex
crusher data.
Co-operation with
the OEM to improve
availability and
uptime means
faster results.
Summary
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