Model Predictive Control For Integrating Processes
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Transcript of Model Predictive Control For Integrating Processes
Model Predictive Control for Integrating ProcessesModel Predictive Control for Integrating Processes
Lou Heavner – Consultant, APC
PresenterPresenter
Lou Heavner
IntroductionIntroduction
Historically, APC project engineers and consultants have tried to keep level control outside of the MPC solution. Level control and control of other integrating processes are poorly understood by many control engineers. This presentation will attempt to answer the following questions:– Can you control level with MPC?– How do you control level with MPC?– When should you control level with MPC?
Integrating ProcessesIntegrating Processes
Non-Self-Regulating – No natural equilibrium or steady-state– Must be controlled– Includes most liquid levels, many gas pressure systems, and
some other processes• Over a short enough time horizon, most processes appear to
be integrating
– Deadtime may be present, but no 1st order or higher order time constants in open loop response
Integrating Process - Open Loop ResponseIntegrating Process - Open Loop Response
Controller Output
Process
Variable
Process ExamplesProcess Examples
Hopper w/ Loss-in-Weight Feeder and Conveyor– Large Deadtime Dynamic
Distillation Column Bottom Level and Reflux Accumulator Level– Multi-variable Interaction
Evaporator Level– Multi-variable Interaction– Large Deadtime Dynamic
Oil & Gas Production Separator Level– Multi-variable Interaction– Slug Control
Conventional Control of Integrating ProcessesConventional Control of Integrating Processes
PI control is recommended– Closed Loop Time Constant (lambda)
• Lambda - (setpoint change) - time for PV to reach setpoint after a setpoint change
• Lambda - (load change) - the time required to stop the change in the PV due to a step load change. The level will return to setpoint in about 6 x Lambda.
• Beall reference describes in great detail
Lambda Tuning Rules (Integrating Process)Lambda Tuning Rules (Integrating Process)
Choose Lambda (λ)– Small Lambda reduces process overshoot and shortens
process response– Small Lambda passes more of the variability “downstream”– Rule of thumb: select Lambda as large as possible to
attenuate process variability
Tr = (2* λ) + Td or if Td<< λ, Tr = 2 *λ Kc = ____Tr____ or if Td<< λ, Kc = ___2____
Kp(λ + Td)2 Kp* λ
Model Predictive ControlModel Predictive Control
●Handles difficult process dynamics, reduces
variability and protects constraints
●Easy, Fast, Implementation
●Fully embedded, no integration required
-Configuration
-Operator Displays
-Historian
●Scaleable, Practical Model Predictive Control
●PredictPro
-LP Optimization
-Large Problems (80x40)
Model Predictive ControlModel Predictive Control
Learns From The Past
To Predict The Future
Learns From The Past
To Predict The Future
Past Present Future
Modeled
Relationship
Multivariable Dynamic Process ModelsMultivariable Dynamic Process Models
The Model Consists Of Step Responses That Show The
Relationship Between Every Process Input And Output
The Model Consists Of Step Responses That Show The
Relationship Between Every Process Input And Output
Model Predictive Control of Integrating ProcessesModel Predictive Control of Integrating Processes
Factors considered:– Feedback mechanism
• Model Correction Factor
• Rotation Factor
– TSS selection– MPC Controller “Tuning”
• POM
• POE
– Multivariable Interaction– Deadtime
Prediction ErrorPrediction Error
Model Correction Factor & Rotation Factor– Consider a prediction vector P whose elements are indexed by j. That
is j= 0 to 119 since in MPC-PRO the prediction horizon is 120 elements long.
– The equation for the update of the prediction vector is:P(j) = P(j) + {(1 – R) + j*R}*F Where R is the ROTATION FACTOR and F is the filtered shift measured as
the error (i.e. the difference between the first element of the last prediction vector and the feedback measurement) multiplied by the MODEL CORRECTION FACTOR
Parameter Names & Default Values– Predict Pro: ROTATION_FACTOR[x] = 0.05– Predict: ROT_FACTOR[x] = 0.001– MOD_CORR_FACTOR[x] = 0.75 v10.0+ or 0.4 in earlier versions– [x] is the number of the process output– Tunable w/o download
MPC TuningMPC Tuning
Time to Steady-State (TSS)– Defines Prediction Horizon– Sets Controller execution speed– Requires Download
Penalty on Move (POM)– Slows the control action of MVs (Process Inputs)– Makes the controller more robust– Powerful, but requires a download to change
Penalty on Error (POE)– Works on Process Outputs– Fine tuning and usually not altered– Requires Download
MPC Pro OperateMPC Pro Operate
SP and Load Response
Effect of TSS SP changesEffect of TSS SP changes
Increasing TSS stabilizes the level control reducing both overshoot and MV moves
CaseTSS
(Configured) "Lambda"Max CV
OvershootMax MV
MoveApparent
TSS
sec min % % min
1 240 9 2.19 5.64 44
2 360 9 0.77 5.17 28
3 600 15 0.38 2.99 33
4 1080 n/a 0 0.36 27
POM = 39.5MCF = 0.75ROT = 0.05
Effect of TSS on load disturbancesEffect of TSS on load disturbances
CaseTSS
(Configured) "Lambda"Max CV
OvershootApparent
TSS
sec min % min
1 240 5 2.44 31
2 360 4 2.03 30
3 600 6 2.99 27
4 1080 6 2.42 32
Setting TSS = 6* Deadtime gives good results approximating 1st
order response
Setting TSS = 10 x Deadtime approaches critically damped response
POM = 39.5MCF = 0.75ROT = 0.05
Load Response with 2 different TSSLoad Response with 2 different TSS
Effect of POEEffect of POE
TSS = 240 secMCF = 0.75ROT = 0.05
Reducing POM improves performance
Case POM "Lambda"Max CV Overshoot
Max MV Move
Apparent TSS
min % % min
1 22 6 1.17 1.82 21
2 39.5 9 2.19 5.64 44
3 55.5 11 2.76 4.44 44
Effect of Model Correction FactorEffect of Model Correction Factor
TSS = 240 secPOM = 39.5ROT = 0.05
Case MCF "Lambda"Max CV Overshoot
Max MV Move
Apparent TSS
min % % min
1 0.5 5 2.11 5.63 33
2 0.75 9 2.19 5.64 44
3 0.9 8 2.12 5.45 32.5
Effect of Rotation FactorEffect of Rotation Factor
Case ROT "Lambda"Max CV Overshoot
Max MV Move
Apparent TSS
min % % min
1 0.01 8 2.22 5.78 44
2 0.05 9 2.19 5.64 44
3 0.1 8 2.18 5.65 32.5
4 0.5 8 2.11 5.47 33
TSS = 240 secPOM = 39.5MCF = 0.75
Lessons LearnedLessons Learned
Select TSS– Limited by Deadtime– Dependant on Self-Regulating responses in multi-variable
application– Nature of desired “closed-loop” response – Tight Response vs
Attenuate Variability– Increase TSS to reduce overshoot
• Start with 6 x deadtime if possible Select Penalty on Move
– Counter-intuitive for integrating processes– Smaller POM reduces overshoot and shortens response
Select Model Correction Factor– Relatively weak handle
Select Rotation Factor– Relatively weak handle
Where To Get More InformationWhere To Get More Information
Author:– [email protected]– (512) 834-7262
References:– Beall, James F., Base Process Control Diagnostics and
Optimization, Internal Emerson document, 2002.
Consulting services– Contact your local sales office