Data driven process understanding
Gunnar Malmquist
GE Healthcare
Uppsala
Sweden
EPSRC Centre User Group Meeting
14 April 2015
Imagination at work.
“Change is one thing,
progress is another.”
Bertrand Russell
Outline
Introduction
Empirical modeling of unit op’s
Beyond unit op’s
The need for mechanistic modeling
The desired destination
Design
Control
Process
Analysis
Understanding
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
3
Today’s approaches are not prepared
for onslaught of Industrial Big Data
Too
slow
Too
expensive
Too
rigid
80% of an analytics project typically involves gathering
and then preparing the data for analysis*
*Source: IDC
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
Data prep pain in social media
”In Data Science, 80% of the time spent
preparing data, 20% of the time spent
complaining about need for preparing data”
BigDataBorat via (dkalab.tumblr.com)
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
5
Imagine instant access…
E.g.
Work Orders
Stock transactions
Recipes
Batch/EBR
Materials Mgmt
Powerful Analytics
Visualization Tools
Process Data
Control
Asset Data
Consumables Traceability
Sensor Calibration
Raw Material Datasets
Probes
Transmitters
BioPharma
Feedstream Data
Incoming CQA
The art of Context
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
Databases
Process Historians
LIMS (QC Data)
Batch Records
6
Unit operation software concept
Process
Equipment agnostic
Cell culture data
Chromatograms
Subsystem
Parameter coding
Analytics
CLOUD
OR
subsystem
ON PREMISE
Parameter coding
GE
Raw material
Subsystem
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
Offline
data
(IPC
etc)
Process data example: 52 runs from PrA CIP study
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
8
First approach: Feature extraction
”Chemometrics for
Characterization,
Classification and
Prediction in
Chromatography”
Gunnar Malmquist
Ph. D. Thesis 1993
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
9
PCA of extracted features reveals grouping
25
24
23
30
6
10
8
7
17
11
12
3
5
13
18
20
14
22
26
37
21
27
31
28 3435
29
32 33
36
47 51
38
48
52
15
50
9
41
40
4
1 2
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
46
42
44
19 16
43
49
45
39
10
Second approach: Use aligned profiles
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
11
PCA on elution phase shows groups w run order
Run#
22
24
21
23 32
27
25
29
28
36
35
26
31
7
12
6
5
3
11
48
46
37
19
33
2
16
51
47
52
50
18
1 4
40
39
20
15
14
17
10
38
9
30
8
34
49
45
13
44
42
41
Outlier run 43 excluded
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
12
Holistic process monitoring
Process overview HIC step
Out of trend
Normal
Multivariate out of trend detection
Drill down to outliers
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
13
Drill down to process data (e.g. chromatograms)
Process overview HIC step
Out of trend
Normal
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
14
Drill down to raw material data (e.g. Resin data)
Process overview HIC step
Raw material overview
Out of trend
Normal
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
15
Operational complexity
Across drugs
Across sites
Across
scales
Process
wide
Unit op
analytics
MFG network
wide analytics
MFG platform
wide analytics
PD to MFG
Wing to Wing
analytics
Need for ”Big Data” tools
Need for mechanistic modeling
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
16
Where do we need to go next?
Empirical models (MVDA) and machine learning
algorithms are powerful tools for process understanding
Domain specific mechanistic modeling adds value
• Transfer of knowledge across scales (PD ↔ MFG)
• Effect of changes in particle size or ligand density etc
• Extra-column effects in chromatography
• Scale-up effects in cell culture
• Identify data transforms driven by domain expertise
• Increases ability to extrapolate
• May increase ability to identify adaptive strategy
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
17
The concept is well known
but still very interesting!
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
Hybrid modeling challenges
Parameter estimation
• Can mechanistic model parameters be estimated from a
large set of process data directly?
• Extract model parameters from process characterisation?
• Are there simple scale down experiments that provide
additional value for mechanistic modeling?
Modeling with uncertainty
• Can mechanistic models provide value when parameter
estimation is less than perfect?
• Add value even if unknowns are present?
An area well suited for a research project ?
Gunnar Malmquist, GE Healthcare, Uppsala, Sweden
EPSRC Centre User Group Meeting 14 April 2015
19
20