application of online data analytics to a continuous process polybutene unit
DESCRIPTION
Continuous data analytics may be used to provide an on-line prediction of quality parameters and enable on-line detection of fault conditions. In this workshop, we present the results achieved in extending Lubrizol’s past work with on-line batch analytics to a continuous polybutene process. Information will be presented on how data analytics may be used to improve multiple quality and operational variables. The presentation will include a demonstration of the web interface used in the field trial and a summary of the operational benefits gained during the trial.TRANSCRIPT
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
2
Presenters
Regina Stone
Robert Wojewodka
Efren Hernandez
Terry Blevins
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.
4
The Lubrizol Corporation Segments
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
Lubrizol Additives Lubrizol Advanced Materials
Growth. Innovation. People.
5
Lubrizol Leading Market Positions
6
Emerson Emerson
DeltaV modules knowledge
Provide Lubrizol with a field trial
tool for online quality parameter
prediction and fault detection
Provide technical support for
difficulties experienced while
using software
Use Lubrizol’s feedback to
further develop the Continuous
Data Analytics software package
Lubrizol Lubrizol
Process and analysis knowledge
Apply software to a continuous
process and identify
measurements
Build models and evaluate and
validate the modeling software
Implement models into ongoing
unit operations
Collect feedback and report
findings to Emerson
Emerson and Lubrizol Roles
Key Goal:
Collaborate with Emerson to develop and test the
Continuous Data Analytics software package.
7
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.
The Setting
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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.
9
General Concepts – A Process
Generic continuous process flow diagram.
INPUTS
PROCESS
OUTPUTS
Very much like batch processing, continuous
process applications can be simplified down to
these major blocks of activity
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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.
11
Basic Concepts
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 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
Univariate SPC Charts
…. Time ….
12
Basic Concepts
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?
Univariate SPC Charts
13
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
XC
TR
= 5
.9426
UC
L =
11
.5478
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
Variable
2
Basic Concepts
14
Multivariate SPC Chart Multivariate SPC Chart Multivariate Control Chart
UCL = 10.77
0 10 20 30 40 50 60
Observation
0
4
8
12
16
20
24
T-S
quare
dMultivariate Control Chart
UCL = 10.77
0 10 20 30 40 50 60
Observation
0
4
8
12
16
20
24
T-S
quare
d
Basic Concepts
…. Time ….
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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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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.
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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.
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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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Preparation for the On-line Trial
Form a multi-discipline team that includes plant operations
Capture team input using
an “input-process-output”
data matrix
Enter lab data
Collect lab data on quality parameters and feedstock
Survey Instrumentation, tune control loops
Conduct formal operator training
This is the same approach that we took for our batch analytics trial several years ago.
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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 measurements
• 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
• 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
• 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 • process data over the full dynamic operating range Collect
• the selected historian data in the CDA application and perform a sensitivity analysis
• the selected historian data in the CDA application 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
• 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
• the web browser to view on-line fault detection and quality parameter prediction; revalidate further once on-line
Launch
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Define Scope of Lubrizol Field Trial
A prototype of a future
DeltaV capability for
continuous process
quality parameter
prediction and fault
detection is being tested
in a field trial at Lubrizol
on:
Polybutene Unit
– Viscosity
Refrigeration System
– Dynamic
Compressor
Efficiency
Refrigeration System
Dynamic
Compressor
Efficiency
Operation 1 Reaction Operation 3 Operation 4
AT
Product
Bulk
Viscosity
Polybutene Unit
2+ Hours
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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
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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.
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Analyze Historian Data
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Generate Model
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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.
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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.
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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.
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Compressor Efficiency Model
30
Model Verification – Compressor Efficiency
Excellent Fit
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.
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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).
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.
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Two Step Monitoring Procedure
– 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.
If a fault is indicted in the analytics overview screen, then select the associated process and the Fault Detection Tab.
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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
36
Installation and Network Setup
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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)
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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:
8-1322 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
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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