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Application of On-line Data Analytics to a Continuous Process Polybutene Unit
An In-Depth Discussion on Model Building
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Presenters
QiWei Li
Efren Hernandez
Robert Wojewodka
Terry Blevins
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Introduction
Continuous data analytics provides online prediction
of quality parameters and detection of fault conditions
Topics
– Introduction of continuous data analytics (CDA)
– Model building techniques and examples
– On-line view platform implementation
– Benefits and achievements
– Q&A
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The Lubrizol Corporation Segments
• 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
Lubrizol Additives Lubrizol Advanced Materials
The Right Mix of People, Ideas and Market Knowledge
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Lubrizol Leading Market Positions
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Lubrizol
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
Emerson and Lubrizol Objectives
Key Goal:
Collaborate to develop and improve the Continuous
Data Analytics software package.
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General Concepts – A Process
INPUTS
PROCESS
OUTPUTS
Very much like batch processing, continuous
process applications can be simplified down to
these major blocks of activity
Generic continuous process flow diagram.
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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.
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SPC Chart for Variable 1
0 10 20 30 40 50 60
Observation
83
86
89
92
95
98
X
CTR = 90.0907
UCL = 96.5239
LCL = 83.6576
SPC Chart for Variable 2
0 10 20 30 40 50 60
Observation
0
2
4
6
8
10
12
X
CTR = 5.9426
UCL = 11.5478
LCL = 0.3374
Anything atypical
with this point? Anything atypical
with this point?
General Concepts – Univariate SPC Charts
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Statistic – Multi-Variant Analysis
SPC Chart for Variable 1
0 10 20 30 40 50 60
Observation
83
86
89
92
95
98
X
CTR = 90.0907
UCL = 96.5239
LCL = 83.6576
SP
C C
ha
rt fo
r V
aria
ble
2
010
20
30
40
50
60
Ob
se
rva
tio
n
02468
10
12
X
CT
R =
5.9
426
UC
L =
11.5
478
LC
L =
0.3
374
Control Ellipse
82 86 90 94 98
Variable 1
-1
2
5
8
11
14
Va
ria
ble
2
Variable 1
Varia
ble
2
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Multivariate SPC Chart Multivariate Control Chart
UCL = 10.77
0 10 20 30 40 50 60
Observation
0
4
8
12
16
20
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T-S
quar
ed
Basic Concepts
…. Time ….
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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.
Online Data Analytics
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Process M1
M2
M3
M4
M5
M6
M7
M8
M9
.... Time Delays
Q1
Q2
Q3
... Online
Measurements
Quality
Parameters from
Lab
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.
X - space Y - space
The Nature of Continuous Data
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Model Building
• the process overview and identify the input, process, and output measurements
Define
• a module that contains a Continuous Data Analytics block and configure for measurements that may impact quality Create
• the module that contains the CDA block and the continuous data historian and begin entering lab data Download
• process data over the full dynamic operating range Collect
• the selected historian data in the CDA application, clean up the data, and perform a sensitivity analysis Analyze
• a model by selecting the state parameter and method. Validate the model for prediction accuracy using data then download the module Generate
• the web browser to view on-line fault detection and quality parameter prediction; revalidate further once on-line
Launch
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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
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Lubrizol Field Trial Scope
Operation 1 Reaction Operation 3 Operation 4
A
Product
Bulk
Viscosity
Polybutene Unit
2+ Hours
Refrigeration System
Dynamic
Compressor
Efficiency
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Refrigeration Model
Quality parameter: Compressor Efficiency
– Maximize compression efficiency
– Detect fault processing conditions
12 out of 22 process inputs incorporated into model
No process delay 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 = 100 ∗ℎ 𝑝2, 𝑠1 − ℎ 𝑝1, 𝑡1ℎ 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
s1=entropy at suction pressure p1 and
temperature t1
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Below models cross verifies predicted (green) against actual (blue)
efficiency
Variation in operating condition lowered model effectiveness
Model Improvement – Example 1
r2=0.97 r2=0.60
2012 Model 2013 Model
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Fault in model contributed by change in feed composition
Re-trained model to reflect feed composition variation in 2013
Model Improvement – Example 1
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The adjacent compressor’s activity had a direct impact on
existing model
Re-train not recommended due to higher frequency of changes
Model Improvement – Example 2
1 Active Compressor
r2=0.05
2 Active Compressor
r2=0.92
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Model Improvement – Example 2
Defined the number of compressors active as “state parameter”
Collect data to cover all process condition variations
Re-train model
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Refrigeration Model Exhibition
r2=0.97
2013 Compressor Efficiency Model
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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
High Vis Input
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Model Improvement – Example 3
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
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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 Y Hour Delay
r2=0.79 r2=0.94
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Combined Viscosity Model Exhibition
2013 Combined Viscometer Model (Cross Verification)
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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)
Jan Mar
Jan 6
420/636 sample points used in model
Mar 1
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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 2013 Model
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Lab Viscosity Model
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.
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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.”
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On-Line View – Quality Prediction
• Prediction is expected to fall within the
confidence bands
• Auto-scaling zooms in to increase
resolution
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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
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Application Station
(existing)
Level 2
Level 2,5
Level 3
Level 4
PI
(existing) A Plant
Corporate
PI LAB DATA
(Matrikon/opc Tunneller)
Web Server
Access
Measurements
(Matrikon/opcTunneller)
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) (existing)
DMZ 2.5
DMZ 2
Production Control Network
Continuous Data Analytics
IIS Server
PureWeb
(existing)
Poly Control Room - On-line
monitoring using Web
Browser
Layers of Network Architecture
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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
Business Results Achieved
Production Detects
Deviation Respond to Deviation
Avoid Lost Production
Time
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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
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Summary
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
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Data Analytics Workshops
Learn more about continuous and batch data analytics by attending the following workshops at this year’s Emerson Exchange:
8-4775 Challenges and Solutions in Data Analytics Application for a Distillation Column
8-4342 How to install Batch Analytics on a non-V12 DeltaV system
8-4240 Application of On-line Data Analytics to a Continuous Process Polybutene Unit
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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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Thank You for Attending!
Enjoy the rest of the conference.