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

1
Presenters




Efren Hernandez



Robert Wojewodka



2

QiWei Li

Terry Blevins
Introduction




Continuous data analytics provides online prediction
of quality parameters and detection of fault condi...
The Lubrizol Corporation Segments
Lubrizol Additives Lubrizol Advanced Materials
•
•
•

Advanced chemical technology for g...
Lubrizol Leading Market Positions

5
Emerson and Lubrizol Objectives
Lubrizol






6

Evaluate the performance of quality
parameter predictions using data
...
General Concepts – A Process
PROCESS
INPUTS

OUTPUTS

Very much like batch processing, continuous
process applications can...
General Concepts – A Process
Initial Conditions
Feed Stock Analysis

Measurements reflecting operating
conditions that imp...
General Concepts – Univariate SPC Charts
SPC Chart for Variable 2

SPC Chart for Variable 1
12

98

UCL = 96.5239
10
CTR =...
UCL = 11.5478
CTR = 5.9426
LCL = 0.3374

Statistic – Multi-Variant Analysis
Control Ellipse
50

SPC Chart for Variable 2

...
Basic Concepts
Multivariate Control Chart

Multivariate SPC Chart
UCL = 10.77
24

T-Squared

20
16
12
8
4
0
0

10

20

30
...
Online Data Analytics


Through the use of Principal Component Analysis (PCA) it is
possible to detect abnormal operation...
The Nature of Continuous Data
Process

M1
M2
M3
M4

Online
Measurements

M5
M6

Q1 Quality
Q2 Parameters from
Q3 Lab
...

...
Model Building
Define

• the process overview and identify the input, process, and output
measurements

Create

• a module...
Model Building


Learning from Data
– Define the key operating regime parameter in the model as the “state
parameter”
– A...
Lubrizol Field Trial Scope
Dynamic
Compressor
Efficiency

Refrigeration System

2+ Hours
A

Operation 1

Reaction

Operati...
Refrigeration Model


Quality parameter: Compressor Efficiency
– Maximize compression efficiency
– Detect fault processin...
Model Improvement – Example 1




Below models cross verifies predicted (green) against actual (blue)
efficiency
Variati...
Model Improvement – Example 1



19

Fault in model contributed by change in feed composition
Re-trained model to reflec...
Model Improvement – Example 2




The adjacent compressor’s activity had a direct impact on
existing model
Re-train not ...
Model Improvement – Example 2





21

Defined the number of compressors active as “state parameter”
Collect data to co...
Refrigeration Model Exhibition


2013 Compressor Efficiency Model

r2=0.97

22
Combined Viscosity Model


Quality parameter: Finished product viscometer output
– Distinguish high and low viscosity cut...
Model Improvement – Example 3





24

Quality parameter (green trend) failed to trigger the selector logic
because the...
Model Improvement – Sensitivity Analysis


Multiple Variables
– Multiple dimensions of data analysis (Time range, time de...
Combined Viscosity Model Exhibition


26

2013 Combined Viscometer Model (Cross Verification)
Lab Viscosity Model


Quality parameter: Finished product lab analyzed viscosity
– Verify viscometer’s accuracy





2...
Lab Viscosity Model – Self Verification



Models are good fit for self verification with existing data
However, next sl...
Lab Viscosity Model




29

Compared to the compressor and combined vis models, the
2013 lab viscosity model requires mu...
Pureweb On-line View


Pureweb on-line view web interface developed by Emerson
– Web interface deployed in process unit f...
On-Line View – Quality Prediction

• Prediction is expected to fall within the
confidence bands
• Auto-scaling zooms in to...
On-Line View – Fault Detection

• Fault is significant if either indicator exceeds a value of 1.0
• Clicking the left fiel...
Layers of Network Architecture
Remote Desktop
Access - Citrix

Beta Team – Model Building using remote desktop,
On-line mo...
Business Results Achieved


Product Integrity and Productivity Improvement
– Quality prediction promotes product integrit...
Business Results Achieved


Operation Efficiency
– A quick glance informs the operators on the state of the process
– Ope...
Summary









36

Continuous Data Analytics has successfully provided
Lubrizol, Deer Park TX with quality predicti...
Data Analytics Workshops
Learn more about continuous and batch data analytics by
attending the following workshops at this...
Where To Get More Information


Regina, Sansoni, T., Blevins, Application of Online Data Analytics to a Continuous Proces...
Thank You for Attending!
Enjoy the rest of the conference.

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Aplication of on line data analytics to a continuous process polybetene unit

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This Emerson Exchange, 2013 presentation summarizes the 2013 field trail results achieved by applying on-line continuous data analytics to Lubrizol’s continuous polybutene process. Continuous data analytics may be used to provide an on-line prediction of quality parameters, and enable on-line detection of fault conditions. Information is provided on improvements made in the model used for quality parameter prediction, and how the field trail platform was integrated into the process unit. Presenters Qiwei Li, production engineer, Efren Hernandez and Robert Wojewodka, Lubrizol Corp., and Terry Blevins, principal technologist at Emerson, won best in conference in the process optimization track for this presentation.

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Aplication of on line data analytics to a continuous process polybetene unit

  1. 1. Application of On-line Data Analytics to a Continuous Process Polybutene Unit An In-Depth Discussion on Model Building 1
  2. 2. Presenters   Efren Hernandez  Robert Wojewodka  2 QiWei Li Terry Blevins
  3. 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. 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. 5. Lubrizol Leading Market Positions 5
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22. Refrigeration Model Exhibition  2013 Compressor Efficiency Model r2=0.97 22
  23. 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. 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. 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. 26. Combined Viscosity Model Exhibition  26 2013 Combined Viscometer Model (Cross Verification)
  27. 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. 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. 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. 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. 31. On-Line View – Quality Prediction • Prediction is expected to fall within the confidence bands • Auto-scaling zooms in to increase resolution 31
  32. 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. 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. 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. 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. 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. 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. 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 38
  39. 39. Thank You for Attending! Enjoy the rest of the conference. 39

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