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1
From Data Overload to Focused Information
Sandeep Lal & Carolyn LehmanChevron Phillips Chemical Co
Sweeny - Texas
Agenda
• Chevron Phillips Chemical Co - Sweeny• Data Overload & the Analysis Required
• Problems with data• Some solutions that we applied
– Control Loop Monitoring & Loop Tuning– Some problems with the solutions– Some solutions to the problems that the solutions generated
•• Experiences of Loop Monitoring & Tuning ImplementationExperiences of Loop Monitoring & Tuning Implementation•• ConclusionConclusion•• Q&A AND DiscussionsQ&A AND Discussions
2
Sweeny
• One traffic light• 1 NGL Unit, 3 Ethylene Units and some pipelines
• 5 LCN– 700 PID controllers in each ethylene unit
• Challenges• Business Environment
– Competitive • APC applications and optimizers have become necessary
– Point Configuration Data, Event Journals, Alarm Analysis
• Manpower (Number and Skill-set)– 2 Process Control Engineers – 1 Applications Engineer
Problems
• Complex Applications• Hardware & Software• Configurations – PCN, DMZ, BN• Maintenance – versions, bugs & features, reports• Backups
• Data Data Everywhere• Who, What & When• Use of Historical Data for Monitoring Applications
– Already happened on Friday @ 5:00 PM– IMPOSSIBLE to keep on top of ALL Loops– Data Compression – Doesn't show the true picture
3
Data Analysis 2002
Compressed dataThrough Historian
Uncompressed Data
Come in Expertune
• Powerful tool for PID Loop monitoring• Now we can monitor LOTS of loop
BUT• Added to problems
• Assesses 30,000,00,000 metrics for each control loop• Have to set baselines and thresholds• How do we visualize the data & analyze it• Develop a work flow
– Analyze, identify problems, fix ….
4
What Metrics to Analyze
1. Variance and Variability2. Error type statistics
1. Average Absolute Error and Average Error 2. Integral Error
3. CO Noise Band 4. Service Factor5. Closed Loop Performance Index6. Oscillation Analysis - Detection and Diagnosis
Configuration
• Setting Baselines & Thresholds • Templates
• Data Visualization & Workflow• Dashboards• Reports (email)
5
Hidden problemDCS Loading
• Should we sample loops @ 1 sec, 2sec, 5sec, 10 sec, 20 sec…
• DCS Loading cannot be studied since it REALLY matters during a unit upset condition• However Load on the DCS can be quantified
Possible Effects of Lower Sample Rates
• Reduce DCS Loading• Save Hard Disk space !!!!• Information Loss
• Will the Performance statistics for loop be effected ?• Model Identification may not be possible
– Process Dynamic information – Correlation Structure, Active Model Capture
• Analysis of Oscillation detection and Loop interactions may be hindered
6
So How Can WeDo This Analysis
• Sample data at different frequencies• Use Excel for
– Statistic Calculations– Frequency Analysis
• Add the SAME Loop to Plant Triage but with different sampling intervals
Design of Experiment
• Q – Does type of loop matter• Flow , Temperature, Level, Analyzer….
• We selected a • Flow loop and sampled it at 2 sec, 10 sec and 20sec• Temperature loop and sampled at 10 sec, 20 sec, 60 sec
7
Temperature LoopStatistics Not Affected
IAE
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Variability
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
OP Std Deviation
0
1
2
3
4
5
6
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Temperature Loop Statistics Affected
Valve Reversals
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Valve Travel
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
SP Crossings
0
50
100
150
200
250
300
350
400
450
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Harris Index
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
8
Temperature Loop Frequency Statistics
Power Period 2
-1,000
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Power Period 3
-2,000
0
2,000
4,000
6,000
8,000
10,000
12,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Power Period 1
-1,000
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Oscillation Strength 1
0
10
20
30
40
50
60
70
80
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Oscillation Strength 2
0
5
10
15
20
25
30
35
40
45
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Oscillation Strength 3
0
2
4
6
8
10
12
14
16
18
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Raw Data – Flow Loop
9
Flow Loops Statistics Not Affected
OP Std Dev
0.0
0.2
0.4
0.6
0.8
1.0
1.2
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Variability
0
2
4
6
8
10
12
14
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Flow LoopStatistics Affected
Harris Index
0.0
0.1
0.1
0.2
0.2
0.3
0.3
0.4
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
SP Crossings
0
200
400
600
800
1,000
1,200
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr0
1,000
2,000
3,000
4,000
5,000
6,000
IAE
30,000
35,000
40,000
45,000
50,000
55,000
60,000
65,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
10
Flow Loop Statistics Affected Cont.
Valve Reversals
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr Valve Travel
0
500
1,000
1,500
2,000
2,500
3,000
3,500
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Flow LoopFrequency Statistics
Power Period 1
0
500
1,000
1,500
2,000
2,500
3,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Power Period 2
0
500
1,000
1,500
2,000
2,500
3,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-AprPower Period 3
-500
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Power strength 2
0
2
4
6
8
10
12
14
16
18
20
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Power strength 1
0
5
10
15
20
25
30
35
40
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
Power strength 3
-2
0
2
4
6
8
10
12
14
28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr
11
Conclusions of Sample Interval Experiment
• Some Statistics are not affected by sampling frequency• E.g .. IAE (?), Variability , OP Std Dev
• Some Statistics are strong functions of sampling frequency• SP Crossings, Valve reversals, Valve travel
– Can we get a multiplication factor to adjust it to the “Faster sampling Domain”
• Frequency analysis is DEFINITELY messed up• Oscillation detection is not possible
Optimum Sampling…Things to Consider
• Aliasing• A false signal that is created as a by-product of sampling and
Fourier transformations
Nyquist Frequency• The lowest sampling frequency must be no less than twice the
highest frequency of interest
13
Lessons Learned
• DO a Baseline study before implementing Loop Monitoring• Benefits
• Reduced Valve wear, Operators like you, APC depends on it…..• Feed the operators.
• Initial Loop Assessment is very important• Positioner, Filter• Behavior in Auto/Cascade• Service
• Talk with the operators• Process knowledge is of utmost importance.
• Have patience
Lessons learned Cont..
• Know the quirks of the software.• Hysteresis & Stiction cannot be simply “tuned out” like
your children at home.• Think outside the box. Perhaps what's needed isn't
tuning but a different control scheme, a different valve, or a different trim, or a different control engineer (don't be afraid to get another pair of eyes involved).
• Document changes – MOC etc….