#PIWorld ©2019 OSIsoft, LLC
Deployment of the PI System and Advanced Analytics in
Manufacturing
Patrick T. O’Sullivan & Erik Klijn
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Erik Klijn
Senior Data Analytics Lead
Leiden
Patrick T. O’Sullivan
Data Science Lead
Cork
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Preculture
Fermentation
Direct Product Capture
Purification
Final testing
General process flow of Monoclonal antibody production
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Current vs Future State
Multivariate Analysis & OSI PI
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Digitalization/SIMCA Deployment
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Forecasting
Digitalisation© 2019 Janssen Supply Group, LLC, and/or its affiliates. All rights reserved. Any use of this material without specific permission of Janssen is strictly prohibited.
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Erik KlijnLeiden, Netherlands
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Last yearAdvanced Analytics facilitates overview
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Warning signal: multivariate (summary) parameter is outside 3SD
Warning signal on spider 83!
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Leiden characteristics
• 30+ bioreactors running in parallel
• Highly flexible plant• A lot of small moveable
equipment• Multiple processes in one
bioreactor hall• Many products in parallel
• Hard to keep overview
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This year
• Optimization of raw material components
• Complex batch genealogy
• Complex cellular interactions with raw material components
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Batch genealogy
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Vendor 1
RM lot1 RM lot2
RM lot3 RM lot4
Med comp 1
Vendor 2
Vendor 3
RM lot1
RM lot1
Med comp 2
BRX 1
BRX 2
BRX 3
BRX 4
Vendor 4 RM lot1
Medium
Medium…
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Optimization of raw material components
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Batch level
advised future models
Drawbacks:
- Only considers batch averages for all continuous parameters
- Raw material component criticality & optimal levels differs over duration of batch
Production data
(MES)
Raw material
component tests
(QC)
Discrete data
Raw material
data per batch
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Optimization of raw material components
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Production data
(MES)
Raw material
component tests
(QC)
Online
measurements
(pH, air flow,
etc.)
(OSI PI)
Discrete data
Raw material
data per batchSIMCA
Batch evolution
advised future models
Continuous data
Advantages:
- Consider real parameters at the right time
- Differentiate between criticality & optimal levels differs over duration of the batch
Tabletop offline
measurements
Discrete data
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% in
cre
ase
in
pro
du
ctio
n
Batch evolution
Optimization of raw material components
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Group 2 (Red):
High production
Group 1 (Blue):
Low production
Top 3 sources of variation (for the entire duration of the batch)
Additional sources of variation (at the end of the batch)
Savings up to 2.5 Million Euro / year
Batch Overview Model Culprit variables Simulated increase in production
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Simulation results % increase in production if main
contributors are altered to optimized valueMain contributors in difference between red and blue
group batches.
Batch selection based on Total IgG distribution(each dot represents a batch, left/blue = low IgG, right/red = high IgG)
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Patrick T. O’SullivanCork, Ireland
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Cork, Ireland
• 5 main products
• 3 1000L BRX
• Set manufacturing
• 600million expansion
• 4 additional 15K
• Clinical Suite17
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DARA PS1 (Cork)
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2-4% Yield© 2019 Janssen Supply Group, LLC, and/or its affiliates. All rights reserved. Any use of this material without specific permission of Janssen is strictly prohibited.
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Why: Non-traditional IEX Behavior
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Harvest break tank weight isn’t something
routinely monitored
MVA Identified the harvest filtrate weights was
abnormally high
Using SIMCA-online, the process SME identified that
it was overfilling
Event Prevention
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$200,000
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Challenges
• Non GMP validated PI system →Data gaps
• Continuous PI data integrated with Discrete Manufacturing and Quality Data
• Transitioning of control systems →Data architecture must be recreated
• Scalability of SIMCA software for multiple models running at one time
• SIMCA-online introduction to Production Areas
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Next Steps
• Transition to PI AF/EF to accommodate for DeltaV
• Introduction of PI vision
• Completion of data architecture and Integration of Azure Data Factory into MfgData Hub.
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Summary
• Real Time monitoring, Forecasting & Simulation Modelling
• Currently active across Janssen’s Large Molecule Platform
• Several process optimisation and yield improvements achieved from MVA
• OSI PI continuous data essential to Batch Evolution Model construction and implementation
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© 2019 Janssen Supply Group, LLC, and/or its affiliates. All rights reserved. Any use of this material without specific permission of Janssen is strictly prohibited.
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Contacts
• Patrick T. O’Sullivan
• Data Science Lead
• Janssen Sciences
• Erik Klijn
• Senior Data Anlaytics Lead
• Janssen Biologics BV
• [email protected]© 2019 Janssen Supply Group, LLC, and/or its affiliates. All rights reserved. Any use of this material without specific permission of Janssen is strictly prohibited.
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Anna Persson, Head of Data Science Sartorius Stedim Data Analytics
Climbing the ladder towards Prescriptive Analytics
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Prescriptive Analytics“How can we make it happen”
“How can we avoid an issue to
happen”Predictive
Analytics“What will
happen”
Foresight
Diagnostic
Analytics“Why did it
happen”
Insight
Descriptive
Analytics“What happened”
Hindsight
The Data Analytics Continuum
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Org
an
iza
tio
na
l an
d b
usin
ess v
alu
e
Data Analytics Complexity
Understand and improve your business Innovate and control your business
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• PISimBatchOL• PI Server and PI Batch Systems
• PIAFSimApi• PI Server and PI EventFrames
Umetrics Suite - Connectors to OSIsoft PI
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Batch evolution Final properties
Multivariate Statistical Process Control(MSPC)
Current Time
Measured Process
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Batch evolution Final properties
MSPC & Process Forecasting
Deviation
Forecast
Current Time
Measured Process
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Batch evolution Final properties
MSPC, Forecast and Advised futureSupervisory Control
Advised future
Forecast
Current Time
Measured Process
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