If there’s contrast enough
NOVEMBER, 2018
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AITalk Berlin
Use cases from the field
Borealis at a glance
2nd largest
polyolefin producer
in Europe
Borouge operates
world’s largest
integrated PO site in
Ruwais, UAE
Head office
in Vienna,
Austria
6,600
employees
EUR 7.5
billion
sales revenue
in 2017
(>EUR 9 billion
with Borouge)
+3,500
in Borouge
Operates in over
120 countries
on 5 continents
Ownership structure
EUR 1,095
million
Production and sales
of polyolefins,
base chemicals
and fertilizers
net profit for 2017
Mubadala, UAE
OMV, Austria
Agenda
01 Selection a viable use case
02 Predictive maintenance
03 Energy Optimisation
04 Key Takeaways
Work package objectives
Selecting a viable use case
Getting ready for the future
Generic approach
Equipment selection decision tree
Structure to select maintenance approach
Initiatives to enable failure prediction on a broader scale
Stakeholder
interviews
Impact assessment
& initiatives
definition
Pilot & Lessons Learned
User journeys
Detailed
initiatives
Project
charters
Select &
execute selected
initiatives
Enablers for failure prediction
Strong contribution to Reliability and Maintenance Excellence
Failure prediction
High quality data
Equipment selection
Continuous
improvement
Alarms and
preventive action
Work processes
Maintenance data
input (in the field)
Consistent root
cause analysis
Data strategy for
critical equipment
Alarm database
Data middleware
Decision tree
Implement
predictive model
Data & tools
Wireless data
network
Asset Portal
Analytics tools
(pattern recognition)
Organisation & competences
Communication
Data fluency training
Visualise model
results
PdM visualisation
(monitoring, POT , KPI)
Enablers – Organisation
• Data driven organisation
•
•
•
Communication on predictive analytics
Mind-set to use data to support decisions
Root cause analysis verified with data (failure
modes)
• Resources and Competences
•
•
User/role specific training
Data analysis and statistics (basic – advanced)
Reliability / Process / TD&E engineers
Right analytics tools e.g. pattern recognition tools
Enablers – Work Processes
• Reliability Management work process
•
•
•
•
Equipment strategies (min. standards/FMEA/maint.tasks)
Equipment monitoring requirements (sensors, condition
monitoring)
Integrate also into design requirements of new investment
projects
Structure to select maintenance approach (decision tree):
Key drivers for equipment selection
• Number of failures
• Data quality & equipment knowledge
• Equipment criticality
• Data set prediction power
• Business impact
Maintenance approach
• Corrective maintenance
• Preventive maintenance
• Predictive maintenance
• Unsupervised anomaly detection
• Supervised anomaly detection
• Maintenance Execution Excellence work process
•
•
Structured maintenance feedback
Efficient and integrated documentation (e.g. work in field)
Enablers – Data
• Wireless technology
•
•
•
Wireless sensors and condition monitoring
Connectivity in the plants
Supporting also work processes
• Centralised data management
•
•
•
Mapping of Visualisation and Asset portal
requirements
Data collection from different sources (data
middleware)
Learning and usage across plants/locations
Hyper Compressor – Data assessment
Dimension
Question asked
Assessment example
Score
Assessment result
Completeness
Are all data sets and data items
recorded?
What is the percentage of missing values?
0.977
Ok
Does the data reflect the data set?
How many outliers are there?
What is the percentage of negative values in positive
defined variables?
How many out-of-range variables are there?
0.968
Ok
Accuracy
Data quality examples
Sensors
VI52523X
VI52524Y
VI52525X
VI52526Y
PDI52034B
PDI52034C
PDI52034A
TI52563
PI52005A
PI52005B
Number of missing Frequency of missing
values
values
319823
319824
319825
319826
205764
205765
194570
51494
41213
41203
Completeness
0.783
0.783
0.783
0.783
0.504
0.504
0.476
0.126
0.101
0.101
Sensor
Number of
negatives
Negative
score
Number of
outliers
Outlier
score
Global
score
FI52524_1F
360778
0.117
8477
0.979
0.548
FI52524_1A
305056
0.253
33714
0.917
0.585
FI52524_1E
309738
0.242
222
0.999
0.621
FI52524_2A
214679
0.475
47977
0.883
0.679
FI52524_2C
TI52572
TI52557
149868
0
0
0.633
1
1
39444
163507
159408
0.903
0.6
0.61
0.768
0.8
0.805
TIC52043_PV
0
1
137106
0.627
0.813
FIC52303_PV
13
1
125544
0.693
0.846
VI525211D
0
1
106266
0.739
0.869
VI525212D
0
1
105082
0.741
0.871
Accuracy
Treatments
See details on next pages
Comparison
Data quality remained similar to
what was found in the previous
assessment focused on data
from 2016
Advanced Analytics – Data Feasibility Results
Work package objectives
Predictive Maintenance on a
hypercompressor
Predictive maintenance – objectives
Increased plant reliability
Use data to work in “earlier part of the P-F curve”
 Failure prediction to prevent unplanned stops
 Intervene pro-actively
• Use data to understand the underlying root causes
 Continuous improvement steered by POT
• Estimate remaining useful life of equipment / asset (component)
•
Improved process efficiency
•
•
Data fluent employees
Aligned work processes to reduce variability
Process Overview
LP Recycle
HP Recycle
B&P Compressor
Hyper Compressor
Reactor
Separation
Hyper Compressor
2 x 6 Cylinders
Plunger
HP Packing
Central Valve
Work process
Connecting the physical and digital worlds to create a smarter business
Continuous and automated
readings by sensors
Data is aggregated
and processed
Pilot progress
Physical
Assets
Analytics reveal
trends and patterns
Processes
Actions are
taken to drive value
Insights allow better
decisions and predictions
Digital
Data science process
Data feasibility
assessment
Equipment selection
Data cleansing
Information
selection
Tags selection
Booster Primary (BP)
Master Data Set
Hyper compressor (HC)
machine
process
total tags
Model assessment
Validation
Failure recognition
Failure classification
Model building
Refine model
Improve model
accuracy
383
730
1113
Failure/Non-failure
time-window
identification
KNN
Logistic regression
Random Forest
total tags 772
Implement model
Going online
Visualise model
outcomes
Dashboard
development
Advanced Analytics – Overview
Hyper Compressor – Normal Operation
Random Forrest – Hyper Compressor – CV
Failure
Outcome & Learnings
Outcomes
During normal machine operation
no noticeable difference between
Homo- and Heteromere
production
Good models detect signal of
failures up to 6 days before a
failure
Component-specific models show
strong accuracy to predict a
failure 14 days in advance
Top 5 learnings
Most likely suitable for
large & complex
problems
Multidisciplinary
expertise required
Good & consistent data
quality is key
High development cost,
low running cost
Scalability depends on
modelling approach &
equipment strategy
Deployment: online model scoring
Modelling
Visualisation
Daily
Top 10
anomalous tags
WS anomaly
Quality checks
KS anomaly
Data Gateway
Hourly
Data pipeline
Site
IP21
Plant
IP21
Quality checks
SQL
Server
DCS
kNN
Feature
generation
Feature
generation
Logistic
Regression
Random Forest
Failure probability
Work package objectives
Energy optimisation in a
chemical plant
Energy optimisation objectives
To increase the energy efficiency of the Kallo
Dehy plant, while maximising the overall
economic value of the plant
• Linking Energy KPI variability to operations
decisions
• Development of dynamic energy target models
Energy streams to be considered are, in
order of priority:
1. Natural Gas, Fuel Gas, Electricity
2. Steam
3. Compressed Air, Cooling water,
Nitrogen
24 |
Approach
ENERGYmaestro
Process &
business
understanding
Economic
mapping &
financial
modelling
Workshops with
operators
Steam
extraction down
Capacity of
steam
extraction
Advanced
analytics
Implementation
preparation &
feedback
Training &
review
Maintenance –
waiting for
spare parts
SO2 alarm
Sulfine plant is
not in operation
Lack of sulfur
There is no
steam for the
turbine
Low pressure
boilers at P2
Boiler BERI
Steam
extraction <
maximum
HP vers LP
Capacity of LP
steam network
Boiler SO2
Consumers
Demand/losses
Start-up valves
Training
Overall
management
Communication
& coordination
2 - 4 weeks
2 - 4 weeks
2 - 4 weeks
4 - 8 weeks
2 - 4 weeks
2 - 4 weeks
Process &
business
understanding
Economic
mapping &
financial
modelling
Workshops
with operators
Implement.
preparation &
feedback
Advanced
analytics
Training &
review
Process understanding
•
•
Site visit and process description
Management
Energy
Monthly
MWh/T C3=
Evaluation of P&IDs / PFDs
•
Evaluation of data availability
•
Data collection
Business understanding
Plant
Electricity
Gas
Steam
LTRS
Weekly
MWh/T C3=
MWh/T C3=
MWh/T C3=
C3= Losses
Production
Electricity
Gas
Steam
LTRS
Daily
MWh/T C3=
MWh/T C3=
MWh/T C3=
C3= Losses
Operations
Air Compressor
Shift
MWh/T C3
Operations
•
•
Identify key financial data and
understanding
Data collection
Real-Time
Key process
variables
Splitter
Compressor
MWh/T C3=
Key process
variables
Charge Heater
Air Heater
Nuovo Pignone
LTRS
MWh/T C3
MWh/T C3=
MWh/T C3
C3= Losses
Key process
Variables
Key process
variables
Key process
variables
Key process
variables
Key process variables to be determined by advanced analytics
Economic
mapping &
financial
modelling
Process &
business
understanding
•
Workshops
with operators
Advanced
analytics
Implement.
preparation &
feedback
Analyse and quantify historical variability against baseline target of initial KPIs.
Training &
review
Process &
business
understanding
Economic
mapping &
financial
modelling
Workshops
with operators
Implement.
preparation &
feedback
Advanced
analytics
Training &
review
•
Workshops with operators and plant staff
•
Brainstorming root causes of variability and process improvement ideas
•
Objective to engage operators in the project
•
Ideas presented on a cause tree
•
Cause tree communicated to operators
Steam
extraction down
Capacity of
steam
extraction
Maintenance –
waiting for
spare parts
SO2 alarm
Sulfine plant is
not in operation
Lack of sulfur
There is no
steam for the
turbine
Low pressure
boilers at P2
Boiler BERI
Steam
extraction <
maximum
HP vers LP
Capacity of LP
steam network
Boiler SO2
Consumers
Demand/losses
Start-up valves
Training
Overall
management
Communication
& coordination
Economic
mapping &
financial
modelling
Workshops
with operators
Advanced
analytics
Implement.
preparation &
feedback
Collaboration with plant staff
Pareto analyses to identify top
influencing parameters
Training &
review
Decision trees to map impact of top
parameters on efficiency
Factor
Yield curve to show change in efficiency
versus multiple variables
Top Explanatory
Variables:
▪ Production rate
▪ Maintenance operations
▪ Impact of input feed quality
▪ Product quality giveaway
▪ Reactor temperature
▪ Steam extraction
▪ Input pressure
Production rate
(T/Hr)
Histograms to diagnose statistical splits
in performance
Number of hours
Process &
business
understanding
Composition (%)
Dynamic Target
Advanced analytics: root causes
• Decision Tree based on good and poor performance periods
• Discussion with process experts to determine controllable variables
Decision Tree for LTRS Ratio (Greater or less than 1.1% Propylene Losses)
30 |
Workshops
with operators
PD
F- X C h a n
Implement.
preparation &
feedback
Advanced
analytics
Training &
review
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Economic
mapping &
financial
modelling
Process &
business
understanding
Engis D1 - Causes de surconsomma on vapeur
Surconsomma on vapeur (EUR)
6000
5000
4000
Oxyda on
3000
Extrac on D1
Défluora on
2000
1000
0
Arrêt
By-pass
Comme
ntaires
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•
Develop useful indicators in control screens
•
Develop daily/weekly/monthly reporting
•
Develop energy implementation roadmap
•
Feedback to operators, plant staff, engineers and managers
Capacité
Process &
business
understanding
Economic
mapping &
financial
modelling
Workshops
with operators
Advanced
analytics
Implement.
preparation &
feedback
Training &
review
Work package objectives
Key Takeaways
Key Takeaways
• Business understanding is key
•
•
•
•
Apply subject matter expertise in data preparation
Reduce number of variables based on SME
Create meaningful features for the problem at hand
Correct the target you want to predict based on SME
• Reduce complexity:
• start with a classifier iso a regressor
• Use explainable models
• Run in iterations (agile approach)
• Always predict a target that can be converted to actions