The document discusses the application of continuous data analytics (CDA) in a polybutene production unit, focusing on model building and the implementation of an online analytics platform for quality parameter prediction and fault detection. Key objectives include evaluating the performance of quality predictions compared to on-line analyzers, enhancing CDA prototypes, and providing operators with tools for improved decision-making in continuous processes. Successful collaboration between Lubrizol and Emerson has led to the development of analytical models that enhance product integrity and operational efficiency.

1. Application of On-line Data
Analytics to a Continuous
Process Polybutene Unit
An In-Depth Discussion on Model Building
1

2. Presenters


Efren Hernandez

Robert Wojewodka

2
QiWei Li
Terry Blevins

3. Introduction


Continuous data analytics provides online prediction
of quality parameters and detection of fault conditions
Topics
–
–
–
–
–
3
Introduction of continuous data analytics (CDA)
Model building techniques and examples
On-line view platform implementation
Benefits and achievements
Q&A

4. The Lubrizol Corporation Segments
Lubrizol Additives Lubrizol Advanced Materials
•
•
•
Advanced chemical technology for global transportation, industrial and
consumer markets
Unique, hard-to-duplicate formulations resulting in successful solutions
for our customers
A talented and committed global work force delivering growth through
skill, knowledge and imagination
The Right Mix of People, Ideas and Market Knowledge
4

5. Lubrizol Leading Market Positions
5

6. Emerson and Lubrizol Objectives
Lubrizol



6
Evaluate the performance of quality
parameter predictions using data
analytics in comparison to the on-line
analyzers in a continuous
polymerization process unit.
Develop simple and informative user
platform for continuous performance
monitoring and data analysis
Evaluate process quality control
schemes based on parameter
predictions using data analytics
Emerson

Support ALL Lubrizol objectives

Test CDA prototype functionality in
collecting data, developing models and
on-line operation

Developing recommendations for
enhancing CDA prototype and future
CDA product

Providing Lubrizol field trial feedback
on CDA for DeltaV product planning
group
Key Goal:
Collaborate to develop and improve the Continuous
Data Analytics software package.

7. General Concepts – A Process
PROCESS
INPUTS
OUTPUTS
Very much like batch processing, continuous
process applications can be simplified down to
these major blocks of activity
7
Generic continuous process flow diagram.

8. General Concepts – A Process
Initial Conditions
Feed Stock Analysis
Measurements reflecting operating
conditions that impact product quality
(X Parameters, In-Process Y Parameters)
Lab Analysis of
Product Quality
(Y Parameter)
Generic continuous process flow diagram.
8

9. General Concepts – Univariate SPC Charts
SPC Chart for Variable 2
SPC Chart for Variable 1
12
98
UCL = 96.5239
10
CTR = 90.0907
LCL = 83.6576
8
95
X
92
6
89
4
86
2
83
0
0
10
20
30
40
50
60
0
10
Observation
Anything atypical
with this point?
9
20
30
40
Observation
Anything atypical
with this point?
50
60

10. UCL = 11.5478
CTR = 5.9426
LCL = 0.3374
Statistic – Multi-Variant Analysis
Control Ellipse
50
SPC Chart for Variable 2
60
14
40
30
8
20
5
Observation
Variable 2
Variable 2
11
10
2
0
2
4
6
8
10
12
0
-1
98
SPC Chart for Variable 1
82
86
90
Variable 1
95
X
98
X
92
94
89
86
83
0
10
10
20
30
40
Variable 1
Observation
50
60
UCL = 96.5239
CTR = 90.0907
LCL = 83.6576

11. Basic Concepts
Multivariate Control Chart
Multivariate SPC Chart
UCL = 10.77
24
T-Squared
20
16
12
8
4
0
0
10
20
30
40
Observation
…. Time ….
11
50
60

12. Online Data Analytics

Through the use of Principal Component Analysis (PCA) it is
possible to detect abnormal operations resulting from both
measured and unmeasured faults.
– Measured disturbances – may be quantified through the application of
Hotelling’s T2 statistic.
• The T2 plot characterizes the amount of process variation that can be explained by the
model and how it compares to “typical” operation.
– Unmeasured disturbances – The Q statistic, also known as the Squared
Prediction Error (SPE) or DMODX, may be used.
• The Q plot characterizes the amount of process variation that cannot be explained by the
model.

12
Projection to latent structures, also known as partial least
squares (PLS) is used to provide operators with continuous
prediction of quality parameters.

13. The Nature of Continuous Data
Process
M1
M2
M3
M4
Online
Measurements
M5
M6
Q1 Quality
Q2 Parameters from
Q3 Lab
...
M7
X - space
M8
Y - space
M9
13
Time Delays
....
In a continuous process there can be a significant differences in the
time required for each on-line measurement to impact processing or
a measured quality parameter.

14. Model Building
Define
• the process overview and identify the input, process, and output
measurements
Create
• a module that contains a Continuous Data Analytics block and configure for
measurements that may impact quality
Download
Collect
• the module that contains the CDA block and the continuous data historian
and begin entering lab data
• process data over the full dynamic operating range
Analyze
Generate
14
• the selected historian data in the CDA application, clean up the data, and
perform a sensitivity analysis
• a model by selecting the state parameter and method. Validate the model
for prediction accuracy using data then download the module
Launch
• the web browser to view on-line fault detection and quality parameter
prediction; revalidate further once on-line

15. Model Building

Learning from Data
– Define the key operating regime parameter in the model as the “state
parameter”
– All variables that impact the quality parameter must be captured
– All variations of the typical operating conditions must be captured
Quality Parameter
State Parameter
Variable X
15

16. Lubrizol Field Trial Scope
Dynamic
Compressor
Efficiency
Refrigeration System
2+ Hours
A
Operation 1
Reaction
Operation 3
Operation 4
Polybutene Unit
16
Product
Bulk
Viscosity

17. Refrigeration Model

Quality parameter: Compressor Efficiency
– Maximize compression efficiency
– Detect fault processing conditions


12 out of 22 process inputs incorporated into model
ℎ
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 = 100 ∗
No process delay
ℎ
𝑝2 , 𝑠1 − ℎ 𝑝1 , 𝑡1
𝑝2 , 𝑡2 − ℎ 𝑝1 , 𝑡1
Where,
h(p2,s1 )=isentropic enthalpy for suction entropy
s1 and discharge pressure p2
h(p2,t2 )=enthalpy at discharge pressure p2 and
temperature t2
h(p1,t1 )=enthalpy at suction pressure p1 and
temperature t1
17
s1=entropy at suction pressure p1 and
temperature t1

18. Model Improvement – Example 1


Below models cross verifies predicted (green) against actual (blue)
efficiency
Variation in operating condition lowered model effectiveness
2012 Model
r2=0.97
18
2013 Model
r2=0.60

19. Model Improvement – Example 1


19
Fault in model contributed by change in feed composition
Re-trained model to reflect feed composition variation in 2013

20. Model Improvement – Example 2


The adjacent compressor’s activity had a direct impact on
existing model
Re-train not recommended due to higher frequency of changes
2 Active Compressor
r2=0.92
20
1 Active Compressor
r2=0.05

21. Model Improvement – Example 2



21
Defined the number of compressors active as “state parameter”
Collect data to cover all process condition variations
Re-train model

22. Refrigeration Model Exhibition

2013 Compressor Efficiency Model
r2=0.97
22

23. Combined Viscosity Model

Quality parameter: Finished product viscometer output
– Distinguish high and low viscosity cuts
– Detect abnormal shift in viscosity
– Detect faulty processing conditions


11 out of 28 process inputs incorporated into model
2+ hour process time delay
Low Vis Input
23
High Vis Input

24. Model Improvement – Example 3



24
Quality parameter (green trend) failed to trigger the selector logic
because the low range viscosity meter’s top limit in the field was lowered
to a value below the selector trigger point
Issue resolved after modifying logic statements
Intimate process knowledge are great supplements for model integrity
upkeep

25. Model Improvement – Sensitivity Analysis

Multiple Variables
– Multiple dimensions of data analysis (Time range, time delay, # of
principle components, etc.)
– One variable is changed at a time to define the optimized model
X Hour Delay
r2=0.79
25
Y Hour Delay
r2=0.94

26. Combined Viscosity Model Exhibition

26
2013 Combined Viscometer Model (Cross Verification)

27. Lab Viscosity Model

Quality parameter: Finished product lab analyzed viscosity
– Verify viscometer’s accuracy



20 out of 28 process inputs incorporated into model
2-3 hour process time delay
Samples are taken every 2 hours (non-continuous)
420/636 sample points used in model
Jan 6
27
Jan
Mar 1
Mar

28. Lab Viscosity Model – Self Verification


Models are good fit for self verification with existing data
However, next slide’s cross-verification shows weaker
extrapolation compared to previously displayed models
2012 Model
28
2013 Model

29. Lab Viscosity Model


29
Compared to the compressor and combined vis models, the
2013 lab viscosity model requires much more work ahead
Possible causes for deviation includes fewer sample points,
additional lab sample delay, missing input variables, etc.

30. Pureweb On-line View

Pureweb on-line view web interface developed by Emerson
– Web interface deployed in process unit for operator to use
– Quality prediction, fault detection, and deviation alarm are featured

Operator Feedbacks:
– “Our plant is getting younger as more experienced operators retire
and new hires come in. The prediction tool will give new operators
a good idea of what they’re making in the reactor and reduce the
chances of making off spec material.”
– “Poly Plant is a complex system. With all the temperature,
pressure, level, and other variables, it is sometimes overwhelming
to run the board. Data analytics will help us focus on the more
important factors that drive the reaction and allow us to make an
informed decision before making a system change.”
30

31. On-Line View – Quality Prediction
• Prediction is expected to fall within the
confidence bands
• Auto-scaling zooms in to increase
resolution
31

32. On-Line View – Fault Detection
• Fault is significant if either indicator exceeds a value of 1.0
• Clicking the left field shows deviations for individual variables
32

33. Layers of Network Architecture
Remote Desktop
Access - Citrix
Beta Team – Model Building using remote desktop,
On-line monitoring using Web Browser/ remote desktop.
A Plant Management - On-line
monitoring using Web Browser
(existing)
PI
(existing)
PI LAB DATA
(Matrikon/opc Tunneller)
Web Server
Access
(existing)
Corporate
Level 4
Poly Control Room - On-line
monitoring using Web
Browser
(existing)
A Plant
Level 3
Continuous Data Analytics
IIS Server
PureWeb
Level 2,5
DMZ 2.5
DMZ 2
Measurements
(Matrikon/opcTunneller)
Application Station
(existing)
33
Level 2
Production Control Network

34. Business Results Achieved

Product Integrity and Productivity Improvement
– Quality prediction promotes product integrity
– Fault detection improves mechanical reliability and prevent
production time lost

Personnel & Process Safety
– Fault detection can recognize abnormal system behaviors and act
as secondary safety safeguards in addition to alarms
Production
34
Detects
Deviation
Respond to
Deviation
Avoid Lost
Production
Time

35. Business Results Achieved

Operation Efficiency
– A quick glance informs the operators on the state of the process
– Operators can utilize future viscosity predictions to reduce transition
time in between product grades

Training
– The web interface is an easy-to-learn training module for less
experienced operators
– CDA provides experienced operator with another layer knowledge
on the intermittent relationship of over 10 variables in a continuous
process
35

36. Summary





36
Continuous Data Analytics has successfully provided
Lubrizol, Deer Park TX with quality prediction and fault
detection capabilities
With the combined effort of Lubrizol and Emerson
representatives, four analytical models were developed for
the compressors and the production unit
These four models were deployed into unit operation
CDA model’s quality prediction and fault detection features
received positive feedback from operations
Extensive knowledge on CDA and model building is gained

37. Data Analytics Workshops
Learn more about continuous and batch data analytics by
attending the following workshops at this year’s Emerson
Exchange:


8-4342 How to install Batch Analytics on a non-V12
DeltaV system

37
8-4775 Challenges and Solutions in Data Analytics
Application for a Distillation Column
8-4240 Application of On-line Data Analytics to a
Continuous Process Polybutene Unit

38. Where To Get More Information

Regina, Sansoni, T., Blevins, Application of Online Data Analytics to a Continuous Process Polybutene
Unit, October, 2012

Terrence Blevins, Willy K. Wojsznis and Mark Nixon Advanced Control Foundation – Tools, Techniques,
and Applications, ISA, 2013

Dunia, R., Edgar, T., Blevins, T., Wojsznis, W., Multistate PLS for Continuous Process Monitoring, ACC,
March, 2012

J.V. Kresta, J.F. MacGregor, and T.E. Marlin., Multivariate Statistical Monitoring of Process Operating
Performance. Can. J. Chem.Eng. 1991; 69:35-47

Dunia, R., Edgar, T., Blevins, T., Wojsznis, W., Multistate Analytics for Continuous Processes, Journal of
Process Control, 2012

MacGregor J.F., Kourti T., Statistical process control of multivariate processes. Control Engineering
Practice 1995; 3:403-414

Kourti, T. Application of latent variable methods to process control and multivariate statistical process
control in industry. International Journal of Adaptive Control and Signal Processing 2005; 19:213-246

Kourti T, MacGregor J.F. Multivariate SPC methods for process and product monitoring, Journal of
Quality Technology 1996; 28: 409-428
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39. Thank You for Attending!
Enjoy the rest of the conference.
39