control loop foundation batch and continuous...
TRANSCRIPT
11/10/2008 1
Control Loop Foundation for
Batch and Continuous Control
GREGORY K MCMILLAN
use pure black and white option for printing copies
11/10/2008 2
Presenter
– Greg is a retired Senior Fellow from Solutia Inc. During his 33 year career with Monsanto Company and its spin off Solutia Inc, he specialized in modeling and control. Greg received the ISA “Kermit Fischer Environmental” Award for pH control in 1991, the Control Magazine “Engineer of the Year” Award for the Process Industry in 1994, was inducted into the Control “Process Automation Hall of Fame” in 2001, and honored by InTech Magazine in 2003 as one of the most influential innovators in automation. Greg has written a book a year for the last 20 years whether he needed to or not. About half are humorous (the ones with cartoons and top ten lists). Presently Greg contracts via CDI Process and Industrial as a principal consultant in DeltaV Applied R&D at Emerson Process Management in Austin Texas. For more info visit:
– http://ModelingandControl.com– http://www.easydeltav.com/controlinsights/index.asp (free E-books)
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See Chapter 2 for more info on “Setting the Foundation”
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See Chapters 1-7 for the practical considerations of improving tuning and valve dynamics
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See Appendix C for background of the unification of tuning methods and loop performance
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See Chapter 1 for the essential aspects of system design for pH applications
Overview
This presentation covers highlights or low lights of current loop performance and how to improve batch and continuous processes:– Pyramid of Technologies– Valve and Flow Meter Performance– Process Control Improvement Examples– Basic Control Opportunities Summary– Reactors and Column Loop Tuning– Facts of Life – Transfer of Variability for Batch– Sources of Disturbances– Transition from Basic to Advanced Regulatory Control of Batch– Online Data Analytics for Batch and Continuous Processes– Virtual Plant– Uses and Fidelities of Dynamic Process Models– What we Need– Columns and Articles in Control Magazine
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Basic Process Control System
Loop Performance Monitoring System
Process Performance Monitoring System
Abnormal Situation Management System
Auto Tuning (On-Demand and On-line Adaptive Loop Tuning)
Fuzzy Logic
Property Estimators
Model Predictive Control
Ramper or Pusher
LP/QP
RTO
TS
Pyramid of Technologies
APC is in any technology that integrates process knowledge
Foundation must be large andsolid enough to support upperlevels. Effort and performanceof upper technologies is highlydependent on the integrity andscope of the foundation (typeand sensitivity of measurementsand valves and tuning of loops)
The greatest success has beenAchieved when the technologyclosed the loop (automaticallycorrected the process withoutoperator intervention)
TS is tactical scheduler, RTO is real time optimizer, LP is linear program, QP is quadratic program
11/10/2008 9
Loops Behaving Badly
A poorly tuned loop will behave as badly as a loop with lousy dynamics (e.g. excessive dead time)!
1Ei = ------------ ∗ Ti ∗ Eo
Ko ∗ Kc
where:Ei = integrated error (% seconds)Eo = open loop error from a load disturbance (%)Kc = controller gainKo = open loop gain (also known as process gain) (%/%) Ti = controller reset time (seconds)(open loop means controller is in manual)
You may not want to minimize the integratederror if the controller output upsets other loops.For surge tank and column distillate receiver level loops you want to minimize and maximizethe transfer of variability from level to themanipulated flow, respectively.
Tune the loops before, during, and after any process control improvements
11/10/2008 10
Unification of Controller Tuning Settings
All of the major tuning methods (e.g. Ziegler-Nichols ultimate oscillation and reaction curve,Simplified Internal Model Control, and Lambda) reduce to the following form for the maximum useable controller gain
max
1*5.0θ
τ∗
=o
c KK
Where:
Kc = controller gainKo = open loop gain (also known as process gain) (%/%)τ1 = self-regulating process time constant (sec)θmax = maximum total loop dead time (sec)
11/10/2008 11
Definition of Deadband and Stick-Slip
Dead band is 5% - 50%without a positioner !
Deadband
Deadband
Stick-Slip
Signal(%)
0
Stroke(%)Pneumatic positioner
requires a negative signal to close valve
Digital positionerwill force valve shut at 0% signal
The effect of slip is worse than stick, stick is worse than dead band, and dead band is worse than stroking time (except for surge control)
Stick-slip causes a limit cycle for self-regulating processes. Deadband causes a limit cycle inlevel loops and cascade loops with integral (reset) action. If the cycle is small enough it can get lost in the disturbances, screened out by exception reporting, or attenuated by volumes
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Saw Tooth Flow Controller Output Limit Cycle from Stick-Slip
Controller Output (%)Saw Tooth Oscillation
Controlled Flow (kpph)Square Wave Oscillation
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Rounded Level Controller Output Limit Cycle from Deadband
Manipulated Flow (kpph)Clipped Oscillation
Controller Output (%)Rounded Oscillation
Controlled Level (%)Saw Tooth Oscillation
11/10/2008 14
Identification of Stick and Slip in a Closed Loop Response
Time ( Seconds )
Stroke%
5353.5
5454.5
5555.5
5656.5
5757.5
5858.5
59
0 100 200 300 400 500 600 700 800
3.25 PercentBacklash + Stiction Controller Output
Flow
Dead band ispeak to peakamplitude forsignal reversal
slip
stick
The limit cycle may not be discernable due to frequent disturbances and noise
11/10/2008 15
Response Time of Various Positioners(small actuators so slewing rate is not limiting)
Response time increase dramatically for steps less than 1%
11/10/2008 16
Control Valve Facts of Life
Pneumatic positioners are almost always out of calibrationMost tests by valve manufacturers for stick-slip are at 50% with loosely tightened stem packing to minimize seating, sealing, and packing friction Without a representative position feedback in the control room, it is anybody’s guess what the valve is doing unless there is a low noise sensitive flow sensorNot all positioners are equal. Pneumatic positioners, especially the spool or single amplification stage low gain ones will increase the valve response time by an order of magnitude (4 -> 40 sec) for small changes in controller outputAll valves look good when checking positions for 0, 25, 75, and 100% signalsValve specs do not generally require that the control valve actually moveThe tighter the shutoff, the greater the stick-slip for positions less than 20%Smart positioner diagnostics and position read back are lies for actuator shaft position feedback of rotary type isolation valves posing as throttling valves particularly for pinned rather than splined shaft connections due to twisting of the shaft. Field tests show stick-slip of 85 in actual ball or disc movement despite diagnostics and read back indicating a valve resolution of 0.5% The official definition of valve rangeability is bogus because it doesn’t take into account stick-slip near the seat. Equal percentage valves with minimal stick-slip (excellent resolution and sensitivity) generally offer the best rangeability
Top Ten Signs of a Valve Problem
(10) The pipe fitters are complaining about trying to fit a 1 inch valve into a 10 inch pipe.
(9) You bought the valve suppliers’ “monthly special.”(8) A butterfly disc won’t open because the ID of the lined pipe is
smaller than the OD of the disc.(7) The maintenance department personally put the valve on your
desk.(6) A red slide ruler was used to size a green valve. (5) Your latest valve catalog is dated 1976.(4) The maintenance department said they don’t want a double
seat “A” body. (3) The valve was specified to have 0% leakage for all conditions
including all signals.(2) The fluid field in the sizing program was left as water.(1) The valve is bigger than the pipe.
11/10/2008 18
Flow Meter Performance
Type Sizes Range Piping Interferences ReproducibilityCoriolis ¼ -8” 100:1 1/1 solids, alignment, vibration 0.1% of rateMagmeter ¼-78” 25:1 5/1 conductivity, electrical noise 0.5% of rateVortex ½-12” 9:1* 10/5 profile, viscosity, hydraulics 1.0% of spanOrifice ¼-78” 4:1 10/5 profile, Reynolds Number 5.0% of span
* - assumes a minimum and maximum velocity of about 1 and 9 fps, respectively
Coriolis flow meters via their accurate density measurement offerdirect concentration measurements for 2 component mixtures andinferential measurements for complex mixtures.
11/10/2008 19
Neutralizer Control – “Before”
Static Mixer
AC 1-1
Neutralizer
Feed
Discharge
AT 1-1
FT 1-1
FT 2-1
AC 2-1
AT 2-1FC
1-2
FT 1-2
ReagentStage 2
ReagentStage 1
2pipe
diameters
11/10/2008 20
Nonlinearity and Sensitivity of pH
pH
Reagent FlowInfluent Flow
6
8
Reagent ChargeProcess Volume
orGood valve resolution or fluid mixing does not lookthat much better than poor resolution or mixing dueamplification of X axis (concentration) fluctuations
11/10/2008 21
Neutralizer Control – “After”
Static Mixer
AC 1-1
Neutralizer
Feed
Discharge
AT 1-1
FT 1-1
FT 2-1
AT 2-1
FC 1-2
FT 1-2
ReagentStage 1
ReagentStage 2
FC 2-1
AC 2-1
20pipe
diameters
f(x)
Σ
FeedforwardSummer RSP
SignalCharacterizer
*1
*1
*1 - Isolation valve closes when control valve closes
11/10/2008 22
Distillation Column Control – “Before”
FC 3-4
FT 3-4
FC 3-3
FT 3-3
LT 3-1
LC 3-1
TE3-2
TC 3-2
LT 3-2
LC 3-2
Distillate Receiver
Column
Overheads
Bottoms
Steam
Feed
Reflux
PC 3-1
PT 3-1
Vent
Storage Tank
Feed Tank
Tray 10
Thermocouple
11/10/2008 23
Nonlinearity and Sensitivity of Tray Temperature
Tray 10
Tray 6
Distillate FlowFeed Flow
% Impurity
OperatingPoint
Temperature
Impurity Errors
Measurement Error
Measurement Error
11/10/2008 24
Distillation Column Control – “After”
FC 3-2
FT 3-2
FC 3-4
FT 3-4
FC 3-3
FT 3-3
FC 3-1
FT 3-1
LT 3-1
LC 3-1
TT3-2
TC 3-2
FC 3-5
FT 3-5
LT 3-2
LC 3-2 RSP
RSPRSP
Distillate Receiver
Column
Overheads
Bottoms
Steam
Feed
Reflux
PC 3-1
PT 3-1
Vent
Storage Tank
Feed Tank
Tray 6 f(x)
Signal CharacterizerRTD
Σ
Σ
FT3-3
FT3-3
Feedforward summer
Feedforward summer
11/10/2008 25
When Process Knowledge is Missing in Action
2-Sigma 2-SigmaRCAS
Set Point
LOCALSet Point
2-Sigma 2-Sigma
Upper LimitPV distribution for original control
PV distribution forimproved control
Extra margin when “war stories” or mythology rules
value
Good engineers can draw straight linesGreat engineers can move straight lines
Benefits are not realized until the set point is moved!(may get benefits by better set point based on process knowledge even if variability has not been reduced)
Top Ten Ways to Impress Your Management with the Trends of a Control System
(10) Make large set point changes that will zip past valve dead band and local nonlinearities
(9) Change the set point to operate on the flat part of the titration curve
(8) Select the tray with minimum process sensitivity for column temperature control
(7) Pick periods when the unit was down(6) Decrease the time span so that just a couple data points are
trended(5) Increase the reporting interval so that just a couple data points
are trended (4) Use really thick line sizes(3) Add huge signal filters(2) Increase the process variable scale span so it is at least ten times
the control region of interest(1) Increase the historian’s data compression so that most changes
are screened out as insignificant
11/10/2008 27
Basic Opportunities in Process Control
Decrease stick-slip and improve the sensitivity of the final element (Standard Deviation is the product of stick-slip, valve gain, and process gain)
Use properly tuned smart positioners, short shafts with tight connections, and low friction packing and seating surfaces to decrease valve slip-stick and dead band (do not use isolation valves for throttling valves)If high friction packing must be used, aggressively tune the smart positionerImprove valve type and sizing and add signal characterization to increase valve sensitivity Use variable speed drives where appropriate for the best sensitivity
Improve the short and long term reproducibility and reduce the interference and noise in the measurement (Standard Deviation isproportional to reproducibility and noise)
Use magnetic and Coriolis mass flow meters to eliminate sensing lines, improve rangeability, and reduce effect of Reynolds Number and piping Use smart transmitters to reduce process and ambient effectsUse RTDs and digital transmitters to decrease temperature noise and drift
11/10/2008 28
Basic Opportunities in Process Control
Reduce loop dead time (Minimum Integrated Error is proportional to the dead time squared)
Decrease valve dead time (stick and dead band)Decrease transport (plug flow volume) and mixing delay (turnover time)Decrease measurement lags (sensor lag, dampening, and filter time)Decrease discrete device delays (scan or update time)Decrease analyzer sample transport and cycle time
Tune the controllers (Integrated Error is inversely proportional to the controller gain and directly proportional to the controller integral time)Add cascade control (Standard Deviation is proportional to the ratio of theperiod of the secondary to the process time constant of the primary loop)Add feed forward control (Standard Deviation is proportional to the root mean square of the measurement, feed forward gain, and timing errors)Eliminate or slow down disturbances (track down source and speed)Add inline analyzers (probes) and at-line analyzers with automated sampling since ultimately what you want to control is a compositionOptimize set points (based on process knowledge and variability)
To realize the benefit of reduced variability, often need to change a set point
11/10/2008 29
Reset Gives Them What They Want
SPPVOut
5244?
TC-101Reactor Temperature
steam valveopens
watervalveopens
50%
Reset won’t open the water valveUntil the error changes sign, PVgoes above the set point. Reset has no sense of direction.
set point (SP)
temperature
time
PV
Should the steam orwater valve be open?
Proportional and rate action seethe trajectory visible in a trend! Both would work to open the water valve to prevent overshoot.
Reset action integrates the numeric difference between the PV and SP seen by operator on a loop faceplate Reset works to open the steam valve
11/10/2008 30
Reactor and Column Loop Tuning
Most reactor and column composition, gas pressure, and temperature loops have too much integral action (reset time too small), not enough proportional action (gain too small), and not enough derivative action (rate time too small).
Rate time should be 0.1x process time constant or 0.1x reset time with a minimum value of sensor lag time.Rate action is essential for exothermic reactors that can runaway
Often these loops are “near integrators” due to a large process time constant . Batch processes often have “true integrators” because of a lack of self-regulation (no steady state). Whether “near integrators” or “true integrators”, these loops require much more gain action to impose self-regulation and provide pre-emptive action. There is a window of allowable gains where too low of a controller gain will result in slow rolling oscillations from reset.
(controller gain) * (controller reset time) > 4 / (integrating process gain)
11/10/2008 31
Modeling and Control Facts of Life
“Timing is Everything”In life, business, and process control (especially feedforward)
“Without Dead Time I would be Out of Job”If the dead time was zero, the only limit to how high you can set the controller gain or how tight you can control is measurement noiseUnlike aerospace, the process industry has large and variable time delays and time lags from batch cycle times, vessel mixing times, volume residence times, transportation delays, resolution limits, dead band, and measurementsTotal dead time is sum of time delays and all time lags smaller than largestBest possible integrated absolute error is proportional to dead time squared
11/10/2008 32
Modeling and Control Facts of Life
Models (experimental or theoretical) allow you to take the blindfold offModels convey process knowledge and provide insight on what has changed and what should be improved (e.g. largest source of dead time)“War stories rule” where there are no models“Mythology rules” where there are no models“Benefits are hearsay” where there are no models
Nonlinearity is a reason to build models rather than avoid modelsUnless you want job security for constantly retuning controllers. Also, implied in most techniques is some model (e.g. reaction curve method)Tight control greatly reduces the operating point nonlinearity (e.g. pH) and secondary flow loops eliminate the valve nonlinearity for higher level loops Signal characterization on the controller output (based on a model of the installed valve characteristic) greatly reduces the valve nonlinearity
11/10/2008 33
Speed of Various Sources of Disturbances(Speed Kills)
ProcessFlow (fast)Gas pressure (fast)Liquid Pressure (very fast)Raw Materials (slow)Recycle (very slow)Temperature (slow)Catalyst (slow)Steam (fast)Coolant (fast)
EquipmentFouling (slow)Failures (fast)
EnvironmentalDay to Night (slow)Rain Storms and fronts (fast)Season to Season (very slow)
A loop can catch up to a slow disturbance. Liquid pressureIs the fastest upset (travels atthe speed of sound in liquid).
11/10/2008 34
Speed of Various Sources of Disturbances(Speed Kills)
ValvesStick-slip (fast)Split Range (fast)Failures (very fast)
MeasurementsNoise (very fast)Reproducibility (fast)Failures (very fast)
ControllersFeedback Tuning (fast) *Feed forward Timing (fast)Interaction (fast)Failures (very fast)
* Most frequent culprit is an oscillating level loop primarily due to excessive reset action
11/10/2008 35
Speed of Various Sources of Disturbances(Speed Kills)
Market*Rate changes (fast)Product transitions (fast)
OperatorsManual operation (fast)Sweet spots (fast)Inventory control (fast)
DiscreteOn-off control (very fast)Sequences (fast)Batch operations (fast)Startup and shutdown (very fast)Interlocks (very fast)
*For minimized inventory, changes in market demand can result infast production rate changes and product grade or type transitions
11/10/2008 36
Batch Control
Variability Transfer from Feeds to pH, and Reactant and Product Concentrations
Feeds Concentrations
Optimum pH
Optimum Product
pH
Product
Optimum Reactant
Reactant
Reagent
Reactant
Most published cases of multivariate statistical process control (MSPC) use the process variables and this case of variations in process variables induced by sequenced flows.
11/10/2008 37
PID Control
Variability Transfer from pH and ReactantConcentration to Feeds
Concentrations
Optimum pH
Optimum Product
Feeds
pH
Product
Reagent
Reactant
Optimum Reactant
Reactant
The story is now in the controller outputs(manipulated flows) yet MSPC still focuseson the process variables for analysis
11/10/2008 38
Model Predictive Control
Variability Transfer from Product Concentrationto pH, reactant Concentration, and Feeds
Optimum pH
Optimum Product
Feeds Concentrations
pH
Product
Reagent
Reactant
Optimum Reactant
Reactant
TimeTime
Model Predictive Control of product concentration batch profile uses slope for CV which makes the integrating response self-regulating and enables negative besides positive corrections in CV
11/10/2008 39
Example of Basic PID Control
feed A
feed B
coolantmakeup
CAS
ratiocontrol
reactor
vent
product
condenser
CTW
PT
PC-1
TT
TT
TC-2
TC-1
FC-1
FT
FT
FC-2
TC-3
RC-1
TT
CAS
cascade control
Conventional Control
11/10/2008 40
Example of Advanced Regulatory Control
feed A
feed B
coolantmakeup
CAS
ratio
CAS
reactor
vent
product
maximum productionrate
condenser
CTW
PT
PC-1
TT
TT
TC-2
TC-1
FC-1
FT
FT
FC-2
<
TC-3
RC-1
TT
ZC-1
ZC-2CAS
CAS
CAS
ZC-3 ZC-4<
Override Control
override control
ZC-1, ZC-3, and ZC-4 work to keep their respectivecontrol valves at a max throttle position with goodsensitivity and room for loop to maneuver. ZC-2 will raise TC-1 SP if FC-1 feed rate is maxed out
11/10/2008 41
Function Blocks for Online Data Analytics
Function blocks developed to support on-line batch and continuous analytics– PCA Block– PLS Block– Analyzer Block
11/10/2008 42
Analyzer Block for Online Data Analytics
History Collection of Lab and Spectral Analyzer Data
Controller
ModuleLab Results
AnalyzerBlock
Historian Operator Station
Off-line Modeling
OtherData
Processing of Sample Data for Use in Analytics
11/10/2008 43
Dynamic Time Warping for Online Batch Data Analytics
Reference trajectoryTrajectory to be synchronizedSynchronized trajectory
11/10/2008 44
Virtual Plant Setup
Advanced Control Modules
Process Models(first principal
and experimental)
Virtual PlantLaptop or Desktop
or Control System Station
This is where I hang out
11/10/2008 45
Virtual Plant Integration
DCS batch and loopconfiguration, displays,
and historian
EmbeddedModeling Tools
Embedded Advanced Control Tools
Dynamic Process Model
Loop MonitoringAnd Tuning
OnlineData Analytics
Model PredictiveControl
Virtual PlantLaptop or DesktopPersonal Computer
OrDCS Application
Station or Controller
Process Knowledge
11/10/2008 46
Typical Uses and Fidelities of Process Models(Fidelity Scale 0 - 10)
Process DevelopmentMedia or reactant optimization and identification of kinetics on the bench top - 10Optimization of process conditions in pilot plant - 9Agitation and mass transfer rates - 8* Process scale-up – 8
* - assumes computational fluid dynamics (CFD) program provides necessary inputs
Process DesignInnovative reactor designs or single use bioreactors (SUB) - 7Vessel, feed, and jacket system size and performance - 6
Automation DesignReal Time Optimization (RTO) - 7Model Predictive Control (MPC) - 6Controller tuning (PID) - 5Control strategy development and prototyping - 4Batch sequence (e.g. timing of feed schedules and set point shifts) – 3
11/10/2008 47
Typical Uses and Fidelities of Process Models(Fidelity Scale 0 - 10)
Online DiagnosticsRoot cause analysis - 5Data analytics development and prototyping - 4
Operator Training SystemsDeveloping and maintaining troubleshooting skills - 4Understanding process relationships - 3Gaining familiarity with interface and functionality of automation system - 2
Configuration CheckoutVerifying configuration meets functional specification - 2Verifying configuration has no incorrect or missing I/O, loops, or devices - 1
11/10/2008 48
Loops that are not islands of automationUnit operation control for integrated objectives, performance, and diagnosticsHigh speed local control of pressure with ROUT, CAS, and RCAS signals
Engineer with process, configuration, control, measurement, and valve skillsVirtual plants with increasing Fidelity (3 -> 7 chemical, 3->10 biological)
Product development, process design, real time optimization, advanced control prototyping and justification, process control improvement, diagnostics, training
Smart wireless integrated process and operations graphicsOnline process, loop, and advanced control metrics for plants, trains, and shifts
Yield, on-stream time, production rate, utility cost, raw material cost, maintenance cost*Variability, average % of max speed (Lambda), % time process variable or output is at limits, % time in highest mode, % deadband, % resolution, number of oscillationsProcess control improvement (PCI) benefits ($ of revenue and costs)
3-D, XY, future trajectories of process and performance metrics response, data analytics, worm plots, and trends of automatically selected correlated variables
Coriolis flow meters, RTDs, and online and at-line analyzers everywhereReal time analysis via probes or automated low maintenance sample systemsAutomated time stamped entry of lab results into data historianOnline material, energy, and component balances
Control valves with < 0.25% resolution and < 0.5% dead band
What Do We Need?
11/10/2008 49
Key Points
Tune the loops Use digital positioners and throttle valves to get resolution better than 0.5%Use Coriolis and Magmeters to get accuracy better than 0.5% of rateTune the loopsAdd cascade and feed forward control for disturbancesModel the process to dispel myths and build on process knowledgeImprove the set points Add composition controlReduce the size and speed of disturbancesTransfer variability from most important process outputsAdd online data analytics (multivariate statistical process control)Add online metrics to spur competition, and to adjust, verify, and justify controls
11/10/2008 50
Control Magazine Columns and Articles
“Control Talk” column 2002-2008“Has Your Control Valve Responded Lately?” 2003“Advanced Control Smorgasbord” 2004“Fed-Batch Reactor Temperature Control” 2005“A Fine Time to Break Away from Old Valve Problems” 2005“Virtual Plant Reality” 2005“Full Throttle Batch and Startup Responses” 2006“Virtual Control of Real pH” 2007“Unlocking the Secret Profiles of Batch Reactors” 2008