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1. Application of Online Data
Analytics to a Continuous Process
Polybutene Unit
Regina Stone Process Improvement Engineer
Robert Wojewodka Technology Manager and Statistician
Efren Hernandez Process Control Superintendent
Terry Blevins Principal Technologist
1

2. Presenters
 Regina Stone
 Robert Wojewodka
 Efren Hernandez
 Terry Blevins
2

3. Introduction
Just as with batch processing, data analytics
can be applied to continuous processes for on-
line prediction of quality parameters and
detection of fault conditions.
In this workshop we present:
 Background and example of continuous data
analytics.
 Field trial of continuous data analytics at
Lubrizol, Deer Park, TX on a polybutene unit
and refrigeration system.
3

4. The Lubrizol Corporation Segments
Lubrizol Additives Lubrizol Advanced Materials
The Right Mix of People, Ideas and Market Knowledge
• 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
Growth. Innovation. People.
4

5. Lubrizol Leading Market Positions
5

6. Emerson and Lubrizol Roles
Emerson Lubrizol
 DeltaV modules knowledge  Process and analysis knowledge
 Provide Lubrizol with a field trial  Apply software to a continuous
tool for online quality parameter process and identify
prediction and fault detection measurements
 Provide technical support for  Build models and evaluate and
difficulties experienced while validate the modeling software
using software
 Implement models into ongoing
 Use Lubrizol’s feedback to unit operations
further develop the Continuous
 Collect feedback and report
Data Analytics software package
findings to Emerson
Key Goal:
Collaborate with Emerson to develop and test the
Continuous Data Analytics software package.
6

7. The Setting
 Operators work in a highly complex, highly correlated and
dynamic environment each day.
 Any advanced warning of deviations is valuable.
 Operators manage a large amount of data and information
on a continuously operating unit. Even with automation,
only so much can be monitored and managed at one time.
 Any help with continuous monitoring across many variables is
valuable.
 The goal is to prevent the undesirable effects of an
abnormal situation by early detection of precursor
deviations and predict product quality real-time.
7

8. Background on Analytic Techniques
 Analytic tools can be applied to both continuous
and batch processes.
 Application to continuous processes require
special consideration such as:
– Varying flow rates
– Product grade transitions
 For model development and on-line use it is
necessary to allow real-time access to
measurements and lab data associated with
product quality and feedstock.
8

9. 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
Generic continuous process flow diagram.
9

10. General Concepts – A Process
Initial Conditions Measurements reflecting operating
Feed Stock Analysis conditions that impact product quality
(X Parameters, In-Process Y Parameters)
Lab Analysis of
Product Quality
(Y Parameter)
Generic continuous process flow diagram.
10

11. Basic Concepts
Univariate SPC Charts
SPC Chart for Variable 1
98
UCL = 9
95 CTR = 9
LCL = 8
92
X
89
86
83
0 10 20 30 40 50 60
Observation
…. Time ….
11

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

13. CTR = 5.9
UCL = 11.
LCL = 0.3
Control Ellipse
Basic Concepts
14
60
SPC Chart for Variable 2
11
50
Observation
Variable 2
40
Variable 2
8 30
5
20
2
10
-1 SPC Chart for Variable 1
0
98
82 86 90 94 98
12
10
8
6
4
2
0
UCL = 96.523
95 CTR = 90.090
X
92
Variable 1 LCL = 83.657
X
89
86
83
Variable 1
0 10 20 30 40 50 60
Observation
13

14. Basic Concepts
Multivariate Control Chart
Multivariate SPC Chart
UCL = 10.77
24
20
16
T-Squared
12
8
4
0
0 10 20 30 40 50 60
Observation
…. Time ….
14

15. The Nature of Continuous Data
Process M1
M2
M3
M4 Q1 Quality
Q2 Parameters from
M5
Q3 Lab
Online M6 ...
Measurements M7
M8
M9
X - space Y - space
....
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.
15

16. The Nature of Continuous Data
The normal operating point of process measurement
may change with process throughput. The
parameter(s) that drive change in the process are
known as state parameters (e.g. production rate). In
this example, the state parameter is the fuel demand.
16

17. The Nature of Continuous Data
Product grade can also be the state parameter in some
cases. A change in the product grade being made is a
change in the state parameter.
17

18. 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.
 Projection to latent structures, also known as partial
least squares (PLS) is used to provide operators with
continuous prediction of quality parameters.
18

19. Preparation for the On-line Trial
Capture team input using Collect lab data on
Form a multi-discipline team quality parameters and
that includes plant operations an “input-process-output”
data matrix feedstock
Survey Instrumentation, Conduct formal
Enter lab data tune control loops operator training
This is the same approach that we took for our batch analytics trial several years ago.
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20. Creating a Data Analytics Model
The following steps are required to develop and deploy a data analytics model:
• the process overview and identify the input, process, and output
Define measurements
• a module that contains a Continuous Data Analytics block and
Create configure for measurements that may impact quality
• the module that contains the CDA block and the continuous data
Download historian and begin entering lab data
Collect • process data over the full dynamic operating range
• the selected historian data in the CDA application and perform a
Analyze sensitivity analysis
• a model by selecting the state parameter and method. Validate the
Generate model for prediction accuracy using data then download the module
• the web browser to view on-line fault detection and quality
Launch parameter prediction; revalidate further once on-line
20

21. Define Scope of Lubrizol Field Trial
2+ Hours
Product
Bulk
A prototype of a future
Viscosity DeltaV capability for
AT
continuous process
quality parameter
Operation 1 Reaction Operation 3 Operation 4 prediction and fault
detection is being tested
Polybutene Unit in a field trial at Lubrizol
on:
Dynamic
Compressor  Polybutene Unit
Efficiency
– Viscosity
 Refrigeration System
– Dynamic
Compressor
Efficiency
Refrigeration System
21

22. Create & Configure Module
1. Create module in DeltaV
Control Studio
2. Configure CDA
block for
measurement
inputs that reflect
conditions that
impact quality
3. Download module and
verify on-line that module
is collecting and calculating
data
22

23. Collect Process Data
Wait for process data to be collected by the historian
that reflect the normal process changes over the full
dynamic operating range.
23

24. Analyze Historian Data
24

25. Generate Model
25

26. Validate Model
 Model is fairly good for the
higher grade of polymer,
but not as good for the
lower grade of polymer.
 Shared this information
with Emerson, who then
made some code
changes in the
software.
26

27. Validate Model Again
 With the code changes,
carefully excluding
outliers, and the data time
delay estimate enhanced,
the model has been
greatly improved.
 Model will be launched for
the online trial after
maintenance turnaround.
 Additional model
development is ongoing.
27

28. Dynamic Compressor Efficiency
 An on-line calculation of 
dynamic compressor
efficiency of both
compressors was
implemented in DeltaV.
 PLS/PCA model for
efficiency prediction and
fault detection were
trained using the on-line
calculation of efficiency.
28

29. Compressor Efficiency Model
29

30. Model Verification – Compressor Efficiency
Excellent Fit
30

31. Process Analytics Overview
 In the Continuous
Data Analytics
Overview screen,
Fault detection
status and quality
parameter prediction
for deployed
PCA/PLS model(s)
are displayed.
 A web browser can
be used to access
this overview if a
station has Ethernet
access to the field
trial station.
31

32. Quality Parameter Prediction
 The impact of process variation on the quality parameters can be seen by
selecting the quality parameter tab to view the predicted quality parameter.
 Predicted values over time can be obtained by clicking in the trend area.
Under normal operating conditions, the predicted value should fall within the
product specification range (green band).
32

33. Fault Detection
 By clicking on a
monitored process
from the overview
and selecting the
fault detection tab,
the calculated
statistics are shown
as Indicator 1 (T2)
and Indicator 2 (Q).
 A fault is indicated if
either statistic
exceeds an upper
fault detection limit
of 1.0.
33

34. Two Step Monitoring Procedure
If a fault is indicted in the analytics overview screen, then select the associated
process and the Fault Detection Tab.
– If either Fault Detection plot
exceeds or approaches the
upper fault detection limit of
1.0, click on that point in the
trend and
• Select the parameter(s)
in the left pane of the
screen that contributed
to the fault
• Evaluate the parameter
trends from process
operation standpoint
• Take corrective action if
necessary.
– Inspect impact of the fault
on quality prediction plot to
find out how quality could be
affected.
34

35. Good Start but More is Needed
 Improve similarity between the Emerson on-line batch and
continuous analytics offerings
 Improve process analysis diagnostics
 Support additional variables in the analysis
 Support “vector data” types (e.g. IR, GC, MS)
 Include Discriminant analysis in addition to PLS in both the batch
and continuous offerings
 Incorporate on-line monitoring of the model’s health
 Implement adaptive updating of a model after initial deployment
 Create the ability to handle select relevant variables when multiple
processing paths may be utilized
 Improve ability to make data available for additional analysis and
model validation outside of DeltaV
 Streamline network access to the online web interface
35

36. Installation and Network Setup
36

37. Summary
 Lubrizol, Deer Park TX is testing a future DeltaV capability for
quality parameter prediction and fault detection for
continuous processes
 Initial assessments indicate that the methodology will be
applicable to continuous processes for:
– Process monitoring and fault detection
– On-line prediction of product quality
– Application to “non traditional” settings such as equipment efficiency
 Good starting point but more needs to be developed to have
these modules applicable for use
 We encourage Emerson to continue their development in this
area to further develop the on-line continuous analytics
module(s)
37

38. Data Analytics Workshops
Learn more about continuous and batch data analytics
by attending the following workshops at this year’s
Emerson Exchange:
 8-1322 Application of Online Data Analytics to a Continuous
Process Polybutene Unit
 8-2092 – Practical considerations for installing and using Batch
Analytics
 8-1965 Batch Analytics Applied to a Large Scale Nutrient Media
Preparation Process
 MTE-4021 Advanced Control Foundation – Tools & Techniques
38

39. Where To Get More Information
 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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