Sitech's process data analytics journey – Application of Trendminer : A user’s perspective Marc Pijpers

1. Sitech's process data analytics journey
Author: Marc Pijpers
© 2017 Sitech All rights reserved

2. Overview
• About Sitech
• The process analytics journey
• Trendminer Use Cases
• Closing Thoughts
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3. Who is Sitech

4. Introduction - Sitech
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• Sitech is an organization with 720 employees which offers services
and solutions to the Site Users of the Chemelot site in Geleen, The
Netherlands, but also more and more to companies located outside
this site.
• Services include
– Maintenance
– Technological improvements
– (Advanced) Process Control
www.sitech.nl

5. Chemelot
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6. Sitech….
• is creating a digital and innovative
culture
• offers a smart service: Sitech Asset
Health Center
• Offers services in maintenance and
process optimization, supported by
process analytics and asset analytics
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7. Supported by today’s technology trends
assets and
process
data
collection
early
warning
visualisationReal time
multi variable
Modelling
Analysis,
decision
& Action

8. Use of extra plant data at low cost
Smart sensors
Gateway
Local server
Office app for analytics
Asset
Health

9. The Process Analytics Journey
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10. 0
500
1000
1500
2000
Annual New Data Stored (Petabytes)
Top 10 Economic Sectors
-McKinsey and
Big Data in Manufacturing

11. 60%
40%
20% 20%
Location
Based Tools
Retail
US
Public
EU
Manufacturing
Manufacturing
The breakdown within
manufacturing tells the real story,
with most of big-data being used in
R & D and less than 5% on the
production line. .
20%
Healthcare
US
Data usage in Manufacturing

12. 80% of our engineer’s problem solving time
is spent on data handling!
Leaving only 20% for the most
value adding steps
The analytics workflow problem
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14. Data science competence dilemma
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15. Positioning self service analytics
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16. Self service analytics vs the data scientist
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Case complexity

17. Self service analytics solutions
• Big data analytics is a journey
• Difficult upfront to specify exactly what you’re looking for.
• Too many solutions out there!
• Pilot  Proof of Concept  Proof of Product  Learn and adapt!
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18. Start of TrendMiner pilot
Requirements
• Easy to use
• Time saver / efficiency enhancer
• Seamless connection with our IT landscape
• Value creation
• Effective and efficient Support
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19. What is TrendMiner
• Trending client with advanced pattern search capabilities on data
historians (i.a. Osisoft Pi, Aspentech IP.21)
– Fast search
– Overlay results
– Compare results using (advanced) statistics
– And many more powerful features
• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
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21. TrendMiner self-service analytics
• TrendMiner is i.a. used for
– Comparison of (similar) situations
– Interactive tool during Root Cause Analysis sessions
– Data cleaning for APC, 6sigma
– Automatic notifications if a specific plant situation occurs
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22. Trendminer Use Cases
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23. Use case 1 - Descriptive analytics
Problem statement
• Too high thermal stress due to
repetitive fast cooling/heating of
reactor
• No monitoring of thermal overload
Goal
• How many thermal cycles result in
failure?
• Inform engineer if reactor has been
thermally stressed
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Temperature profiles on the east and
west side of the reactor of interest

24. Use case 1 - Descriptive analytics
Application of TrendMiner
• Search for periods (5 years) where
derivative of reactor temperature is
too high
– < 5 minutes work!
• Export search results to perform
lifespan residue analysis
Value
• Analysis Time saver
• Automatic Monitor informs process
engineer and material specialist of
thermal overload
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Derivative Temperature profiles

25. Use case 2 - Diagnostic analytics
Problem statement
• Chemical to be washed out, shows peak after washing unit
• Can cause too high temperatures to downstream process
• Steam consumption increased (€)  problem still remained
Goal
• Determine the root cause of the chemical peak and gain process
knowledge
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26. Use case 2 - Diagnostic analytics
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27. Use case 2 - Diagnostic analytics
Application of TrendMiner
• Search/compare peak periods
vs normal operation periods
• Limited influence of extra
steam
• New influencing process
variable identified
Value
• Increased stable operation 
increased production
• Decreased steam
consumption
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Annual benefits: 1,8 M€

28. Use case 3 - Diagnostic analytics
Problem statement
• Product flow peaks 
– saturated sensor
– manual correction KPI
reporting.
Goal
• Determine the root cause
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29. Use case 3 - Diagnostic analytics
Application of TrendMiner
• Perform 2 stage influence factor analysis on peak
• Root Cause identified: Operating window led to exceedance of
demister capacity.
Value
• Improved process insight
• Reduction of manual labor cost
• Improved process stability
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30. Use case 3 - Diagnostic analytics
Application of TrendMiner
• Perform 2 stage influence factor analysis on peak
• Root Cause identified: Operating window led to exceedance of
demister capacity.
Value
• Improved process insight
• Reduction of manual labor cost
• Improved process stability
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31. Closing Thoughts
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32. The TrendMiner journey
• Proven efficiency and value
adding tool for the subject
matter expert
• Pilot  Scale-up & continuation with TrendMiner
• Continuation of process engineer on demand.
– A service that Sitech also considers to provide
• Complimentary to TrendMiner, other analytics
solutions are investigated/piloted.
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20% spend on data
handling!
Leaving 80% for the most value adding steps

33. Questions
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