© abb - 1 - cpmplus loop performance manager 3.2 introduction to lpm
TRANSCRIPT
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cpmPlus Loop Performance Manager 3.2
Introduction to LPM
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Presentation Outline
Introduction / Motivation
cpmPlus LPM Features Tuning
Control Performance Monitoring
Supporting Utilities
cpmPlus LPM Plant-wide Disturbance Analysis
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cpmPlus Loop Performance Manager
1. Introduction/Motivation1. Introduction/Motivation
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Why Loop Performance Monitoring?
“Does my plant run optimally?” If not, how much can be accounted to
the process automation, especially the control loops?”
We should use available measurement data instead of just storing it.
Normal operation does not necessarily mean optimal operation
Loop optimization saves money without new capital investments
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Real world performance is suboptimal!
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An investment that has to pay off!
Typical control loop as a $25,000 asset Half of it is lost
50 % well tuned 25 % uneffective control 25 % decrease performance
Half time of good performance = 6 months 2 – 4 hours to investigate and improve control
performance Typical process contains 2000 – 4000 control loops Only few people with appropriate know-how Average process engineer in charge of 400 control
loops 25 % of 4000 loops do harm, this means…
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Analysts start to get the message
Quotes: “…while process equipment is an integral part of AM [asset management] programs, control
loops … often don’t receive the same attention.”
“Performance of control loops … degrades slowly over time with little fanfare.”
“Without properly tuned control loops to minimize variability, … substantial benefits are lost.”
“… even a slight degradation in process control can result in millions of dollars in lost profitability.”
“Identifying the high-payback control loops requires evaluating all control loops, which would be an insurmountable task without the aid of control loop performance monitoring and analysis software.”
“When first installed, advanced process control typically provides substantial benefits. Sustaining those benefits due to changing conditions, however, has been a problem.”
“… it’s a good time to ensure control systems are part of your AM efforts.”
Recent issue:
“Include control loops in asset management”
Les A. Kane, Editor
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Benefits of Tuning and Auditing
Maintains control system at its peak
Loop Tuning
Enables the plant engineers to reach loops optimum performance with significant time savings (vs. manual tuning)
Loop Auditing
Provides timely indication of equipment/automation/process problems. In this way it easy to keep the loop at their , allowing to stay at the optimal performance
Also, it provides stable foundation for multivariable/advanced control
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cpmPlus Loop Performance Manager – What is it?Loop Tuning Challenge
Optimal PID Tuning is critical to efficient process operation Loop Tuning is a time consuming activity Typically, only expert engineers can perform Tuning
Solution LPM Tuning makes definition of optimal PID parameters an easy,
reliable & manageable task
Loop Auditing Challenge
Loop optimization is frustrating, because after few months all results seem lost due to the process variability
Plant engineers have to look at hundreds of signals and among them detect possible problems
Solution Once Loop Optimization is performed, LPM Auditing monitors loops and
allows the process engineer to immediately address problem areas
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Cost of bad control
High
LowTime
Dream
Co
st
High
LowTime
Reality
Co
st
High
LowTime
Realistic dreamwith Auditing
Co
stLoop Tuning Execution
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cpmPlus Loop Performance Manager
2. LPM Features2. LPM Features
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LPM Tuning – Workflow Which step to tune a Loop?
Configure
Collect
Model Tune
Log
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LPM Features – Data Collection Configure database
by loops
Simultaneous data collection for multiple loops
OPC connectivity
Direct connection for Infi90/Symphony
Data collections stored as object on navigation tree for future retrieval
Possibility to exploit auditing automatic data collection for tuning purposes
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LPM Tuning - Identification BASIC for not experts and
ADVANCED with fully scalable complexity for expert control engineers
BASIC
ADVANCED
Manual or Automatic structure selection by best fit
Parameters specified - up to 4th order
Identification also with Process in Close Loop
Validation
Model simulated with another data set
Evaluation
Ideal step response
Bode diagram
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LPM Tuning
5 Tuning methods available Time domain analysis
Frequency analysis
Support many vendor specific PID controller types
Ability to model, tune, and analyze Feedforward control loops. Considers feedback tuning.
Special treatment of Cascade control loops
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LPM Advanced Tuning Features
New Tuning values can be assessed on model different from the ones used to obtain the tuning set (Simulate Mode)
Data pre-processing functionalities
Advanced Feedforward Loop Tuning Management
HTML-based and information-richer Tuning Logs
Advanced Cascade Loop Tuning Management
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LPM Tuning – Advantage State of the art Tuning Algorithm, but with
user-friendly tool to make Advanced Control Theory accessible to every Plant Engineers
Ready for every DCS OPC connection
Calculated PID parameters (Kp, Ti, Td ) with the definition of your DCS
Identification also with Loop in normal Close Loop Mode
Not only basic PIDs, but also FeedForward and Cascade Loop
Control Tuning becomes easy, fast, profitable
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Performance Assessment: Tuning vs. Auditing
Tuning - Design stage Assessment stage
Reasonable design
Slightly tight design
?
Is this good control?
If not: why?
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Control loop monitoring – non-invasive!
indices
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LPM Auditing - General concept
based on available signals only(SetPoint, PV, CO)
available information can be incorporated
performance indices, measures
inference engine
suggest remedies
know howInfo
Hypothesis, Diagnosis
know how
I1, I2, I3, …
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Kinds of Performance Indices in LPM
Basic statistics
Data Validity
Control loop modes
Tuning Performance indices
Oscillation indices
Valve indices
Measurement“PV”
Measurement“PV”
Target“SP”
Target“SP”Controller
Output “CO”
ControllerOutput “CO”
Nonlinearity indices Property indices Housekeeping Special indices Continuous indices
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Kinds of Diagnoses in LPM
Performance indicesPerformance indices
Auditing RulesAuditing Rules+ Maintenance DiagnosesMaintenance Diagnoses
Indices plus know-how organized in a Root-Cause analysis elaborate Maintenance Suggestions
Diagnoses dealt with problems in: Tuning, Actuators and Sensors, External disturbance
Diagnoses
Tuning Problem
Loop Oscillatory
SetPoint oscillatory
Significant external disturbance
Significant non-linearity
Valve stiction
Valve leakage or zero error
Valve size incorrect
Excessive valve action
Data unrealiable
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Overall Performance
Acceptable performance indexHarris index
Acceptable setpoint crossings indexSetpoint crossing index (not for Level Control)
Variability randomOscillation index of control error
Controller output within rangeSaturation index
Loop automaticAutomatic mode index
Acceptable cascade trackingCascade tracking index (if in cascade)
Acceptable response speedACF to horizon index
Acceptable performance indexHarris index
Acceptable setpoint crossings indexSetpoint crossing index (not for Level Control)
Variability randomOscillation index of control error
Controller output within rangeSaturation index
Loop automaticAutomatic mode index
Acceptable cascade trackingCascade tracking index (if in cascade)
Acceptable response speedACF to horizon index
Acceptable Overall performanceAcceptable Overall performance
excellent
good
fair
poor
PRECONDITIONS
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Auditing workflow
Loop configurationAssign• TAG connection• Signal ranges• Loop Type
Auditing configurationAssign• Data collection schedule• Batch / continuous auditing
Loop category configurationAssign• Sampling rate• Batch duration
Report configurationAssign• Report layout
Configuration file
Configuration
Indices ReportsExcel, HTML
Diagnoses ReportExcel, HTML
Indices Trend PlotOutputPeriodical reports
Maintenance Operator•Repair device•Tuning
Process Engineer•Investigate Problem•Activate Maintenance Maintenance
Start auditing
DatabaseData collectionIndices calculation
Setpoint CO,PV
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Example oscillation investigation...
F
FC
static friction
cycling load
tight tuning
Diagnoses
Verify overall Performance
Detect oscillation
Decide among the 3 causes
Indices
Oscillation details (period, amplitude…)
Amount of problem for every causes
Trend plot for every index
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LPM Auditing - KPI Reporting & Analysis
Reporting
Pre-defined report templates
Both numerical and chart-based assessment
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Advanced Auditing Features
Advanced Indices & Diagnosis trend facility (on multiple even non consecutive periods)
User-defined Indices
Enhanced KPI and Diagnosis set
Server Status Monitor to supervise all the auditing functions
“What Is Changed” report to immediately eye-catch recently developed events
Possibility to generate a “Detailed Loop” Report, with in-depth charts and numerical figures
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Detailed Report Time domain view
(PV,SP,CO)
Power spectrum view (PV)
Statistical view (PV, CE)
CE vs. CO, during oscillation becomes a ring. From the shape it is possible to detect stiction
Impulse response of Disturbance Rejection
Sensitivity study for Prediction horizon (good situation when lines is increasing with steps)
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And More …
Operation-Sensitive Reports: allow to monitor control loops according their operating region(s)
Examples: production campaign types, loads, …
Capability to extract and utilize for Tuning purposes data automatically collected during Auditing normal operation
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Bulk Database Import for quick DB Configuration
Allows to import tag configuration details from Excel spreadsheets
Results in Relevant Manpower Savings
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Infi90/AC800F Bulk Import Tool
Available as an add-on to standard LPM Functions
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LPM auditing - Everything also by Web
Facility to get and manage all LPM
information from any location in the net
From the LPM Home Page it is possible to navigate to …
… Reports Configuration …
… Reports Retrieval …
… Tuning Logs
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LPM Auditing: Advantages Automatic data-collection enable actual
continuous loop performance assessment rather than “sporadic sampling”, maximizing the chance to identify and correct insurgent production-related problems
Simple, straightforward diagnostic indications are made available for the basic user or for quick assessment
Diagnostic results are based on sophisticated indices which are able to provide explanations or in depth analysis for advanced user or when needed
Both Diagnosis and Indices are saved and stored in user-configurable Reports so to not require continuous attention from plant crew and to provide a comprehensive “plant history” track record
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cpmPlus Loop Performance Manager
3. Plantwide Disturbance 3. Plantwide Disturbance AnalysisAnalysis
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Plant-wide disturbance analysis - intro
Analysis process data off-line
Searches for data pattern in time (oscillations) and frequency (specra) to identify Oscillations
Interactions
Identifies most likely root-cause (with no info on plant topology/interconnections)
Integrated in LPM, could use auditing data or external data (e.g. plant historian)
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Plant-wide disturbance analysis - intro
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FC
LC
FC
FC
FC
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TI
TI
FC
FC
LC
19
4
FC
Internal Condenser
Co
lum
n 1
20
TI
PI
TI
TC
Steam Steam
Co
lum
n 2
PI
Internal Condenser
PDI
TC
TI
TI
TI
TI
PI
PDI
DecanterLC
TC
TI
TCSteam
1
3
1
1
5
4
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1
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39
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59 3
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PDA Application – Case 1
Cascaded Distillation Columns:
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PDA Application – Case 1: Dataset Details
Primary cycle Column 1 level through column
2 distillate
Cause is LC2 valve movement problem
Many variables cycling together
Secondary cycle Top of column 1 (distillate
FC2 and temperatures)
Cause is FC2 valve movement problem
96 hours total data, sample time = 30 sec
Dataset window chosen
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PDA Application – Case 1: Clustering
Three main Clusters detected: Two Oscillation
Clusters
One PCA Cluster
A few tags have been added to clusters due to process considerations
Oscillation Clustering: manually added 1 related tag to grouping (primary cycle)
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PDA Application – Case 1: Clustering
Three main Clusters detected: Two Oscillation
Clusters
One PCA Cluster
A few tags have been added to clusters due to process considerations
Default grouping: secondary cycle, had to add ti2.pv and ti3.pv tags manually
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PDA Application – Case 1: Clustering
Three main Clusters detected: Two Oscillation
Clusters
One PCA Cluster
A few tags have been added to clusters due to process considerations
PCA cluster default grouping, manually added 2 related tags to grouping (primary cycle)
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FC
LC
FC
FC
FC
22
32
10
TI
TI
FC
FC
LC
19
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FC
Internal Condenser
Co
lum
n 1
20
TI
PI
TI
TC
Steam Steam
Co
lum
n 2
PI
Internal Condenser
PDI
TC
TI
TI
TI
TI
PI
PDI
DecanterLC
TC
TI
TCSteam
1
3
1
1
5
4
3
1
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1
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PDA Application – Case 1: Main Clustered Disturbances
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Good default results for non-linearity analysis (primary cycle) (ranks LC2 as highest non-linearity)
PDA Application – Case 1: Root Cause Analysis
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FC2 cycle (secondary cycle) analysis: non-linearity correctly identifies FC2
PDA Application – Case 1: Root Cause Analysis
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FC
LC
FC
FC
FC
22
32
10
TI
TI
FC
FC
LC
19
4
FC
Internal Condenser
Co
lum
n 1
20
TI
PI
TI
TC
Steam Steam
Co
lum
n 2
PI
Internal Condenser
PDI
TC
TI
TI
TI
TI
PI
PDI
DecanterLC
TC
TI
TCSteam
1
3
1
1
5
4
3
1
21
1
2
39
1
2
2
3
4
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6
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59
3
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PDA Application – Case 1: Disturbance Propagation
Cluster 1Cluster 2
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LC
Liqu
id
LC
PC
Liqu
id
LC
FC
Liqu
id
Steam
LC
FC
Liqu
id
A B DC
SteamSteamSteam
7 PI
7
7
2
2
5
5 9
PI
9
9
Vapor Header
PC1
SP
PC
PDA Application – Case 2
Vaporizer System:
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Cycle of interest
Two main Clusters detected: One Oscillation
Clusters
One PCA Cluster
A few tags have been added to clusters due to process considerations
PDA Application – Case 2: Clustering
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8 Good results for non-linearity, clearly identifies LC2 as root cause
PDA Application – Case 2: Root Cause Analysis
Ref. to: “Peak Performance: Root Cause Analysis of Plant-
wide Disturbances”, ABB Review 1/2007
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cpmPlus - LPM Conclusions
Tuning With LPM Process Engineers (also
non expert in control theory) can optimize Loop behavior
Benefits: increase process profit, more stable working condition, more safety operations
PDA Very valuable insight on process
corrrelations, oscillations and root causes with a few points and click
Could use your historian data (with reasonable data compression)
Complementary to tuning and auditing
Auditing Control Performance Monitoring is
non-invasive, simple to perform and very efficient
LPM detects automatically problem at the beginning of their occurrence
Performance monitoring nowadays answers the most important questions to help the plant personnel to pinpoint and remove problems
The right information to the right people