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Process Analysis Using 3D Plots . Dr. Frank Seibert, University of Texas. Presenters. Frank Seibert, University of Texas Terry Blevins, Principal Technologist Julian Post, Paul Muston, Mark Nixon. Introduction. - PowerPoint PPT Presentation
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Process Analysis Using 3D Plots
Dr. Frank Seibert, University of Texas
Presenters
Frank Seibert, University of Texas
Terry Blevins, Principal Technologist
Julian Post,
Paul Muston,
Mark Nixon
Introduction The benefits of 3-D plots in data analysis and history collection
of array parameters have been demonstrated in a field trial. In this presentation we addressed:– Target applications – Analysis of high speed processes, distributed
processes and data from spectral analyzers.
– Historian modifications/design for support of arrays data.
– Web enabled 3-D plotting, how array support was used to improve update performance.
– Field Trial at University of Texas, Pickle Research Center where absorber temperatures were analyzed during startup 3-D plotting and .
The technical feasibility of providing 3-D plotting and historian collection of array data has been explore and the value of such a capability proven in two of the target application.
Analysis of High Speed Data Analysis DeltaV supports measurement and
control execution at speed as fast as100 msec.
To enable samples as fast as 100msec to be trended and analyzed, samples may be collected at the controller as an data set/ array and communicated to the historian.
Values are saved in historian and accessed as though they had been reported at the module execution rate.
Module executing at 100msec was created to place high speed data into an array parameter. Arrays communicated at a much slower rate e.g. once per 2 sec.
Controller
Historian
Application station
Analysis Tools e.g.
Entech Toolkit
Var 10 PI4735A.PV - Ind. DO1204AA.datPrimary Cleaner Feed Pressure 05/29/2001 14:15:14
Time Series
0.00 102.40 204.80 307.20 409.60Sec
35.75
37.16
38.57
39.98
41.39psig
Mean=38.1345 2Sig=1.671 (4.38%)
Var 10 PI4735A.PV - Ind. DO1204AA.datPrimary Cleaner Feed Pressure 05/29/2001 14:15:14
Power Spectrum (FFT)
0.00 6.00 12.00 18.00 24.00Cycle/Sec
0.0000
1.9535
3.9069
5.8604
7.8138Variance (E-3)
0
25
50
75
100% Variance
De-Trend=No, Win=None, Seg=0Var 10 PI4735A.PV - Ind. DO1204AA.datPrimary Cleaner Feed Pressure 05/29/2001 14:15:14
Auto Correlation (FFT)
0.0 3.2 6.4 9.6 12.8Sec
-1.0
-0.5
0.0
0.5
1.0
Var 10 PI4735A.PV - Ind. DO1204AA.datPrimary Cleaner Feed Pressure 05/29/2001 14:15:14
Power Spectrum PeaksDe-Trend=No, Win=None, Seg=0
Lower Threshold: 1.703E-3, Change Threshold: 2.044E-3
Total Variance: 0.69770 % Total P-P 2 SigmaPeak Freq. Period Shape Variance Amplit. Remain.
1 0.018785 53.234 4 3.557 0.44559 1.64062 0.039546 25.287 2 1.246 0.26373 1.66013 0.38086 2.6256 1 0.8886 0.22271 1.66314 0.70557 1.4173 1 0.8553 0.21849 1.66345 0.062533 15.992 2 1.294 0.26870 1.65976 0.19043 5.2513 1 0.7231 0.20090 1.66457 0.36664 2.7275 4 1.797 0.31669 1.6555**Truncated**
Profession /Operator
Station
Standard Trend – 100msec
Resolution
Array/Data Set
Distributed Process
Temperature and/or pressure distribution across a process unit is often important from an operation perspective.
Univariate plots are ineffective in finding problems
3-D plots show relationship of measurements and how this relationship changes with time
TTTT TTTT
TTTT TTTT
TT
TT
TT
TT
TT
TT
TTTT TTTT
TTTTTT TT
Spectral Analyzers
Spectral analyzers may be used at critical points throughout the process.– Pharmaceutical - inspection of feedstock,
blend uniformity, granulation, drying and coating and particle size analysis. Online QA/QC tool for production.
– Chemical - acid value, adhesive content, cure, melt index, and polymer processes -reaction monitoring
– Refinery, petrochemical - fuel production monitoring
A wide variety of commercial on-line, at-line, and laboratory spectral analyzers are available.
Calibration of an NIR analyzer is based on use of spectral data to develop principal component analysis(PCA) and projection of latent structures (PLS) models.
Example: NIR Analyzers Careful development of a set of
calibration samples and their use in PCA/PLS model development is the basis for near-infrared analytical methods.
For purposes of analysis, the spectral data for a sample should be saved and accessed as one set of data e.g. an array.
3-D plotting of spectral data can be helpful in screening samples and in analyzing on-line use of spectral data.
Off-line PCA/ PLS Model
Development
On-line Quality Parameter
Prediction
Historian
Array/Data Set
Application station
NIR Analyzer ControllerVIM Interface
3-D Plot of Spectral
Data
Field Trial - DvCH Array Handling Enhancement All samples for an array are held
in the database under a single tag, to enable high access speed (minimization of seek times) or logical grouping of data (e.g. spectral analysis)
Implementation caters for use of successive elements of array for different measured variables or of the same measured variable (high speed trending)
Designed also for use of 2-D array to hold several successive scans of several different measured variables
2-D array, variable
per column
Variable per
row entry
Single
Variable
Time
Time
Time tagging in Controller
Array time tagging at Controller – high resolution, improving time-stamp accuracy
Faster sampling rates possible (scan periods down to 100ms)
PHV display of array tag
Field Trial Design Considerations Designed to allow full incorporation in DvCH product Existing facilities to be supported for each individual measured
variable in scanned array, as if configured individually for history collection
Existing read of tag for specified interval, applied to tag for first element of array, gives all samples for array within time interval in time-stamp order (including array index part) so – Samples in time order– Samples at same time in array-index order
Hence DvCH clients (e.g. PHV, DeltaV Reporter and OPC HDA) can meaningfully access history without code change. (See PHV display on previous slide).
Easy to extend client-interface for individual measured variable access etc. e.g. 3-D plotting app
3D Plotting Technology Various approaches may be
taken in visualizing 3D data. Surface Plot, Bar Plot , Series Plot , Line Plot, Scatter Plot, Wirefame Plot, Mesh Plot
Limited number of commercially available components for use with a web browser.
Wire frame plot has may advantages in viewing and analyzing date.
3D Bar Plot
3D Wire Frame and Surface Plot
3D Plot Capability for Field Trial
A 3-D wire frame plot component was selected for the field trial.
Used to visualize absorber temperatures collected by the historian as array data.
Interface may be accessed using web browser.
3-D plot interface supports rotation and easy access to individual measurement values
Controls Provided for 3D Plot
Selection of Absorber or Stripper
3D views from different angles
Selection of time span and resolution
Move back in forth in time
Array data used for 3D plots was saved in DeltaV Continuous Historian
3D Views Supported
View1 View2 View3
Time Span Selections
1 Hour15 Minute 4 Hour
SRP CO2 Capture Pilot Plant
16
Gas Capacity, m3/min = 25
Solvent Capacity, liter/min = 130
Inlet CO2 Composition, mol% =1-20
Capabilities:
- Solvent Screening
- Packing Performance
- Effect of Absorber Inter-cooling
- Solvent Regeneration Variations
- Evaluate Process Dynamics
- Evaluate Heat Exchangers
- Model Validation
Field Trial - UT/SRP CO2 Capture Process
Stripping ColumnColumn Diameter, cm = 42.8 Packed Height, cm = 600 Pressure, bar = 0.2-4Provides for Flashing FeedWindows for ObservationKettle ReboilerShell and Tube CondenserPlate and Frame Cross Exchanger11 bar Saturated Steam10 C Chilled Water
Absorption Column Column Diameter, cm = 42.8 Packed Height, cm = 600 Pressure, bar = 1 Windows for Observation Inter-cooling Capability Extensive Temperature
Measurements
Absorber Intercooling Process Flowsheet
Absorber Intercooler Skid
Absorber: No Intercooling
22
Absorber Intercooling Operation
MEA Absorber Temperature ProfileL/G = 4.8 (lb/lb), 415 ACFM, 0.3 Lean Loading
40.0 45.0 50.0 55.0 60.0 65.0 70.0 75.0 80.00
5
10
15
20
25
30
Temperature (°C)
Elev
atio
n fr
om A
bsor
ber
Bot
tom
(ft
)
No Intercooling
86.5% Removal, Run 1
Intercooling (40°C)
93% Removal, Run 2
35.0 40.0 45.0 50.0 55.0 60.0 65.0 70.0 75.00
5
10
15
20
25
30
Temperature (°C)
Elev
atio
n fr
om A
bsor
ber
Bot
tom
(ft
)
MEA Absorber Temperature ProfileL/G = 2.1 (lb/lb), 500 ACFM, 0.2 Lean Loading
No Intercooling
83.2% Removal,
Run 11
Intercooling (40°C)
82.5% Removal,
Run 12
Effect of Absorber Inter-cooling
26
Parameter Without Inter-
cooling
With Inter-
cooling
% CO2 Removal 87 93
Stripper Efficiency, kJ/kg CO2
4,170 4,015
Absorber 8-27-2010 1:27-5:25pm View 1
Absorber 8-27-2010 1:27-5:25pm View 2
Absorber 8-27-2010 1:27-5:25pm View 3
Absorber 8-27-2010 4:27 – 8:25pm View 1
Absorber 8-27-2010 4:27 – 8:25pm View 2
Absorber 8-27-2010 4:27 – 8:25pm View 3
Stripper 8-27-2010 1:28 – 5:28pm View 1
Stripper 8-27-2010 2:28 – 6:28pm View 2
Stripper 8-27-2010 2:28 – 6:28pm View 3
Stripper 8-27-2010 3:28 – 8:26pm View 1
Stripper 8-27-2010 4:28 – 8:26pm View 2
Stripper 8-27-2010 4:28 – 8:26pm View 3
Business Results Achieved
3-D plots and historian support of array data is being used to analyze absorber column temperature variation during startup. It is expected that insight gained through the use of 3-D plotting on-line will lead to a reduction in startup time
Analysis of high speed data associated with liquid pressure/flow loops is planned and should lead to improvements in the process operations.
Summary
3-D plotting based on historian collection of array data can be used to analyze distributed process and spectral data.
Trending of high speed data is possible if data is collected at the controller in an array.
The benefits of 3-D plotting of absorber temperature and trending of high speed data will be demonstrated at the UT Pickle Research Center.
Field trial work to demonstrate 3-D plotting and historian collection of array data sets a foundation for future enhancement in DeltaV.
Where To Get More Information
Graphis 2D and 3D graphing software - Scientific/engineering graph plotting and visualization. http://www.kylebank.com/
Data Visualization – On-line Samples, http://samples.infragistics.com/