Optimization Services, Tools and Advanced Process · PDF fileOptimization Services, Tools and...
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§© ABB Service Optimization Services, Tools and Advanced Process Control
Transcript of Optimization Services, Tools and Advanced Process · PDF fileOptimization Services, Tools and...
§© ABB Service
Optimization Services, Tools andAdvanced Process Control
© ABB Service
Outline
§ Optimization Services and Tools
§ Loop Performance
§ Batch Optimization
§ Boiler Fuel Savings
§ Sustaining Performance
§ ServicePort
§ Advanced Process Control
§ Predict & Control
§ APC Applications
§ New Technology Developments
© ABB Service
Optimization Service Methodology
Goal: Measure and Reduce Performance Gap, Extend system life,Operate at the mechanical system constraints
Proc
ess
Perf
orm
ance
Pote
ntia
l
Time (years)
100 %
ManualAuto
Ideal
Implement2.
Diagnose(Fingerprints)
Performance Gap
1.
Startup Continued Operation
Sustain(ProcessPRO)
3.
§© ABB Group§October 29, 2013 | Slide 4§© ABB Group§October 29, 2013 | Slide 4
ABB Fingerprint finds gaps, develops customer ROI
Data Collection/Testing§ 12 to 24 hours at 5-second data§Controller parameters§Customer interview: process areaand loop criticality definitions.
Performance Evaluation§Standard Methodology§Analysis Expertise§Performance Visualization
Report§Gap Analysis§ROI Forecast§Action Plan
BenchmarkROI Findings Plan
OPCCollectionTools
People
Process
Tools
Report
Action
Problem
Interpret
Analyze
View
Get
§© ABB Group
§October 29, 2013 | Slide 5
© ABB GroupOctober 29, 2013 | Slide 5© ABB GroupOctober 29, 2013 | Slide 5© ABB GroupOctober 29, 2013 | Slide 5
ABB Advanced Services: Fingerprints are packageddiagnostic services
§Common Industrial Services :§ Boiler§ Alarm§ Loop Performance§ Control System§ Transition Analysis§ Batch Analysis§ Tuning
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ABB Automation & Power World - May 18-20, 2010
CCH-101-1 11:00 Tuesday, room 351C
Doug Reeder, Ted MatskoLoop Performance Fingerprint for aDuPont Monomers Plant
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LoopAnalyzer Tool: Control Loop Diagnoses
CONTROL PROCESS SIGNAL CONDITIONING
C1: Manual P1: FCE Out of Range S1: Quantized
C2: Oscillating Setpoint P2: FCE Size S2: Excessive Noise
C3: Error Deadband P3: FCE Problem S3: Spikes
C4: Offset P4: FCE Leakage S4: Step Out
C5: Over Control P5: Intermittent Disturbance S5: Data Compression
C6: Slow Control P6: Persistent Disturbance S6: Over Filtered
C7: FCE Travel P7: Questionable S7: Sampling Rate
C8: Slow Update Rate S8: No Signal
C9: Questionable Control S9: MV Out of Range
S10: Questionable
FCE = Final Control Element
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Loop FingerprintOscillating Loops
§ When a loop oscillates inautomatic mode and there isnot evidence of an externaldisturbance, over tuning is apossible cause.
§ Power spectrum shows twofrequencies of interest
§ This loop is in the TFESynthesis area
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Loop FingerprintFinal Control Element Problem
§ This is a flow controller that is the inner loop of a cascade.
§ Exhibits classic stiction
§ Controller output ramps up and down in triangular pattern
§ Process variable moves in square wave
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Loop FingerprintReport
§ The report highlights some loops, as shown in the previousslides and summarizes the results in tables. This table isfor the TFE Synthesis section of the plant.
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Loop FingerprintPlantwide Disturbance Analysis
§ Disturbances in chemical plants act on many process variables
LC1
TI1
TI7
DP1
TI6
TI2TI3
DP2
TI4
LI2TI5
TC1
§ Disturbances can propagate counter flow because of recycle andthermal integration
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Loop FingerprintPCA Cluster Example
§ Find signals withsimilar patterns,probably due todisturbances
§ Not looking foroscillating signals
§ Here all signals arein two columns thatare adjacent
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Increasedperformance
1
Service
2
3
Diagnose(Fingerprints)
Implement(HandsOn)
Sustain(Scan/Track)
© ABB GroupOctober 29, 2013 | Slide 13
Loop Optimization implements improvements
Goal: Improve current performance level§ Scope based on Diagnose phase
§ Focused, scheduled activities
§ Proven practices
§ ABB-managed improvement program
§ Solution categories:§ Hardware§ Operations§ Tuning§ Application§ Process
2
Proactive and collaborative
Workbench: Implementation improvements
§© ABB Group§October 29, 2013 | Slide 14
Analysis
Identification
Tuning and Simulation
Tool WorkbenchStandard Reporting
Visualization
and Setup
Collection
2
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© ABB Service
C549I11:15 Wednesday, April 25, 2012 (room 371D)
Batch Process OptimizationBASF Polymerization Reactor
ABB Automation & Power World: April 23-26, 2012
© ABB Inc.October 29, 2013 | Slide 16
BASF Batch Reactor OptimizationPolyeseter – Key Equipment in “I” Reactor System
§ 5,000 gallon reactor with Therminol-66 heating system§ Fractionator column§ Condenser and Decanter for water collection
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© ABB Inc.October 29, 2013 | Slide 17
BASF Batch Reactor OptimizationTemperature Control Loop Performance
§ Looking at standard data, the oscillations are difficult to seebecause of the wide range of operation typical in batchprocesses and different starting times
§ Define steps (events)
§ View by event
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© ABB Inc.October 29, 2013 | Slide 18
BASF Batch Reactor OptimizationProcess Economics - Quality Control and Production Rate
Process Economics is tied to two things
§ Quality Control§ Decrease cost, i.e. lower energy
§ Increase yield at same quality
§ Reduce offspec losses
§ Value increases with quality
§ Production Rate§ Hold fixed costs constant
§ Increase production rate, revenue
§ For a batch process, production rate means cycle time
§ For this polyester product, Step 4 dominates
© ABB Service
© ABB Inc.October 29, 2013 | Slide 19
BASF Batch Reactor OptimizationProcess Economics - Quality Control and Production Rate
§ Quality variance is very low for this product
§ Batch held until all specs metèincrease time
§ Lab tests repeated, manual adjustments
§ This plant is Production Ratelimited on this product
§ Long cycle time
§ 5 day x 24 hr work week
§ 120 hrs working time
§ 60 hrs = 2; 40 hrs = 3 batches
§ Opportunity
§ Reduce varianceof step times
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© ABB Inc.October 29, 2013 | Slide 20
BASF Batch Reactor OptimizationReducing Batch Cycle Times
§ This plot confirms conclusion about vacuum and batchcycle time
§ Real data is not always pretty (scatter)§ Due to lab measurement, operator manual
operations, unknown contaminants
Step4 Elapsed Time(Vacuum)
Conclusion:Investigate cost toimprove vacuum
Achieved cycle timeimprovements
© ABB Service
© ABB Service
ABB Automation & Power World
CSE-102-1: Boiler FingerprintSuccess Story: How Arkema Saved$300,000 per year on Energy
Dwight Stoffel, Bob Horton
ABB Optimization Services
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Boiler Fingerprint : Value
§ Energy Savings
§ More Responsive to Process SteamDemands
§ Extended Operating Range
§ More Reliable
§ Improved Safety
§ Reduced Carbon Footprint
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FD Fan Control – Combustion Air
30
31
32
33
45.0
47.5
50.0
52.5
2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600
Raw Data
Sample Number, Ts = 5 , Total Samples = 10801
A-FIC-201(PV) A-FIC-201(SP) A-FIC-201(C)
Positioner and Dampers = Suspect
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Boiler Hardware Issues
• Positioner drives not operating smoothly.• Cylinder/Piston assemblies should be rebuilt or replaced.• Motors are oversized.
Forced Draft Control Drive and fan Induced Draft Control Drive and fan
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Hysteresis – Air and Fuel
800
900
1000
10
1214
20253035
15.017.520.022.525.0
4
6
8
300 400 500 600 700 800 900 1000 1100 1200
Hysteresis in Fuel and Air Controls14-May-08 PM Data Set
Fuel
Out
put
Air
Out
put
O2
Data Points
A-FIC-330(PV) A-FIC-330(SP) A-FIC-330(CO) A-FIC-330(CO) A-FIC-300(PV)
A-FIC-300(SP) A-FIC-300(CO) A-FIC-300(CO) A-AT-315 A-AT-315
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Updated Air Curves
Steam to Trim O2 Curve
2
3
4
5
6
7
8
9
10
10 20 30 40 50
Steam Flow
O2
Setp
oint
O2 Trim (Original)O2 Trim (New)
Gas Fuel for Air Curve
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
10 15 20 25 30 35 40 45 50
Steam Flow (KLB/Hr)
Dem
and
forA
irFl
ow
Air (Original)Air (new)
Oil Fuel for Oil Curve
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
10 15 20 25 30 35 40 45 50
Steam Flow
Dem
and
forA
irFl
ow
Air (Original)Air (new)
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Boiler #2: Customer results
Boiler 1 O2 vs Load1 Hour Avgs.
2
3
4
5
6
7
15 20 25 30 35 40
Steam Load
%O
xyge
n March 2008February 2009February All AutoFuel Savings
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Financial Impact – Boiler #2
Boiler #2 rated at 40 klb steam/hr
Savings range of 2% to 3% achievedwithout major capital
Approximate value = $75K to $100K forBoiler No. 2 alone
Savings for all four boilers = $300,000
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Increasedperformance
1
Service
2
3
Diagnose(Fingerprints)
Implement(HandsOn)
Sustain(Scan/Track)
© ABB GroupOctober 29, 2013 | Slide 29
Loop Optimization helps sustain customer results
Goal: Maintain improved performance level
§ Adjust maintenance operating procedures
§ Adjust standard operating procedures
§ Remote process monitoring
§ Specifics are a function of theImplement phase
§ Periodic monitoring of keyprocess indices utilizing localor remote expertise
3
Proactive and collaborative
© ABB Service© ABB GroupOctober 29, 2013 | Slide 30© ABB GroupOctober 29, 2013 | Slide 30
ABB ServicePortTM
Secure, remote delivery for Scan and Track
§ Secure portal residing at customer sitethrough which plant personnel andABB experts can access:
§ Configuration tools
§ Diagnostic applications
§ Improvement activities
§ Performance-sustainingtroubleshooting
§ Scanning software that deploysagreed actions.
§ ABB can connect to any systemthrough ServicePort and implementfixes to diagnosed problems.
Desk top
Full Size
ServicePort + Channels
© ABB GroupOctober 29, 2013 | Slide 31
§ ServicePort Base Unit
§ Process Channels
§ Control Loop
§ Quality Control System
§ Mine Hoist
§ Equipment Channels
§ Harmony
§ 800xA
§ Cyber Security
§ Remote Access Platform
© ABB Service
Outline
§ Optimization Services and Tools
§ Loop Performance
§ Batch Optimization
§ Boiler Fuel Savings
§ Sustaining Performance
§ ServicePort
§ Advanced Process Control§ Predict & Control
§ APC Applications
§ New Technology Developments
ABB’s Predict and Control APC Solution
§© ABB Service
Features
§ True Multivariable Control
§ State Space, Model Predictive Control (MPC) Structure
§ Subspace and Prediction Error model identificationmethods
§ Constraints (MVs and CVs): prioritize up to 30 classes
§ OPC connectivity to process data
§ Inferential Modeling Platform (IMP) to infer variables orproperties that are difficult to measure
Predict and Control: Engineering Tool
§© ABB Service
§ Data import, analysis and trending
§ Allows process model development
§ Shows interaction effects among variables
§ MPC simulation to simulate control and benefits
Optimize IT Predict & Control Performance§ 3 simulation runs of the HOF problem
§Comparingtypical DMCtreatment of
disturbances tostate feedback,using the Shell
Heavy OilFractionatorproblem withunmeasured
disturbances andmodeling error
§1st exampleDMC like tuning,
compositions(blue and red)
have largedeviationscaused by
unmeasuredcooling duty
disturbances,(magenta line,
lower grid)
§2nd example,P&C with
Kalman Filterestimate for
disturbances.
§Large reductionin PV deviation.
§
§3rd example,P&C with KalmanFilter estimate for
disturbances.
§Using additionalPVs
§Furtherreduction in PV
deviation.
§
§© ABB Service
Inferential Modeling Platform Model Builder
Ø Data Import
Ø Data Analysis & Preprocessing
Ø Modeling functions are providedby proven, field-tested, latestgeneration routines
Ø Model development is executedthrough Wizards, to reduce effortfor inexperienced users
Ø The Model Explorer facilityallows off-line use of models forengineering purposes
IMP Online Features
ü Quick and effective real-time implementation on differentDCS through OPC
ü Configurable filtering of inputs and outlier removalstrategies
ü Direct connection toLaboratory InformationManagement Systems
ü Built-in functions forperiodic recalibration (Biascalculation)
Predict & Control PerformanceAmmonia Stripping Operation APC
§Minimizes operating cost (steam, caustic, &acid)
§Regulates pH and NH3
§Enforces environmental limits on wastestream
§9% reduction in unit operating cost
ROI: 6 months
Predict & Control PerformanceButadiene Purification Process APC
ROI: 11 months
§ Reduction of steam consumption
§ Reduction of solvent use
§ Lower product quality variability
§ Key process variables closer tosetpoint targets
§ Less work load for operators at alllevels
§© ABB Service
Alumina Refinery Powerhouse Optimization
§© ABB Service
Alumina Refinery Powerhouse Optimization
Results
§ 80% reduction in header pressure standarddeviations
§ Reduction in cascaded boiler trips, reducingoutage time and production losses
§ Savings in energy costs alone provided ROIwithin 6 months
© ABB Service
APC Example: Zellstoff CelgarSupervisory Control Architecture
§ Multi-Layered Solution
OPTIMIZATIONLAYER
ADVANCEDCONTROL ANDMONITORING
LAYER
BASECONTROL
LAYER
PROCESS
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DMCplus 800xA Interface
o ABB Interface tags defined in 800xA to linkDMCplus tags to control loop and analog tags
o 800xA library created and can be reused fordifferent DMCplus applications
Optimizer
Controller
SubControl
DMCContTypeMV
DMCSubType
FForCV
DpaPidCC
ReadMMSWrite MMS
RealIOor Real
MV DpaAnalogOutCC
(Future)
MMS
ReadMMS
InOut
RealIOMMSRead orRealMMSRead
© ABB Service
DMCplus 800xA Interface
MV Faceplate
New ABB MPC Project Workflow
§ Controller design off line
§ Perform plant testing
§ Use Model Builder to create model
§ Use closed loop simulation module to calculate initialset of tuning parameters
§ Export model and parameters
§ Controller implementation in DCS
§ Create application in Control Builder
§ Import model and tuning parameters to service usingPlant Explorer
§ On-line tuning using console faceplates
Model and Control Design Tool
§ Environment for handlingdata import and data setmanipulation
§ Subspace identificationand graphical methods formodelling
§ Initial controller design
§ Controller export forsubsequent import in runtimeenvironment
Controller Configuration
§ Control Module for MPC
§ Configured from ControlBuilder or FunctionDesigner
§ Control connectionsbetween to level 1controllers
§ Faceplates for interaction
§ Mode logic
§ Constraint definitions
§ Options to connect signals
§ Graphical connections
§ Textual connections
PV and MV Faceplates
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Summary
§ Optimization Services
§ Fingerprint diagnostic services
§ Implementation services
§ Sustaining services with ServicePort
§ Workbench Tools
§ ServicePort Channels
§ Advanced Process Control
© ABB Service
