PID Control of True Integrating Processes - Greg McMillan Deminar

Interactive Opportunity Assessment Demo and Seminar (Deminar) Series for Web Labs – PID Control of True Integrating Processes Aug 11, 2010 Sponsored by Emerson, Experitec, and Mynah Created by Greg McMillan and Jack Ahlers www.processcontrollab.com Website - Charlie Schliesser (csdesignco.com)

Welcome
Gregory K. McMillan
Greg is a retired Senior Fellow from Solutia/Monsanto and an ISA Fellow. Presently, Greg contracts as a consultant in DeltaV R&D via CDI Process & Industrial. 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, was honored by InTech Magazine in 2003 as one of the most influential innovators in automation, and received the ISA “Life Achievement Award” in 2010. Greg is the author of numerous books on process control, his most recent being Essentials of Modern Measurements and Final Elements for the Process Industry. Greg has been the monthly “Control Talk” columnist for Control magazine since 2002. Greg’s expertise is available on the web site: http://www.modelingandcontrol.com/

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And the Number 1 Reason :

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Demos of Control Loop Opportunities to Reduce Fed-Batch and Startup Time
PID on Error Structure
Maximizes the kick and bump of the controller output for a setpoint change.
Overdrive (driving of output past resting point) is essential for getting slow loops, such as vessel temperature and pH, to the optimum setpoint as fast as possible.
The setpoint change must be made with the PID in Auto mode.
“ SP track PV” will generally maximize the setpoint change and hence the kick and bump (retaining SP from last batch or startup minimizes kick and bump)
SP Feedforward
For low controller gains (controller gain less than inverse of process gain), a setpoint feedforward is particularly useful. For this case, the setpoint feedforward gain is the inverse of the dimensionless process gain minus the controller gain.
For slow self-regulating (e.g. continuous) processes and slow integrating (e.g. batch) processes, even if the controller gain is high, the additional overdrive can be beneficial for small setpoint changes that normally would not cause the PID output to hit a limit.
If the setpoint and controller output are in engineering units the feedforward gain must be adjusted accordingly.
The feedforward action is the process action, which is the opposite of the control action, taking into account valve action. In other words for a reverse control action, the feedforward action is direct provided the valve action is inc-open or the analog output block, I/P, or positioner reverses the signal for a inc-close.

Demos of Control Loop Opportunities to Reduce Fed-Batch and Startup Time
Full Throttle (Bang-Bang Control) - The controller output is stepped to it output limit to maximize the rate of approach to setpoint and when the projected PV equals the setpoint less a bias, the controller output is repositioned to the final resting value. The output is held at the resting value for one deadtime. For more details, check out the Control magazine article “ Full Throttle Batch and Startup Response. ” http://www.controlglobal.com/articles/2006/096.html
A deadtime (DT) block must be used to compute the rate of change so that new values of the PV are seen immediately as a change in the rate of approach.
If the total loop deadtime (  o ) is used in the DT block, the projected PV is simply the current PV minus the output of the DT block (  PV) plus the current PV.
If the PV rate of change (  PV/  t) is useful for other reasons (e.g. near integrator or true integrating process tuning), then  PV/  t =  PV/  o can be computed.
If the process changes during the setpoint response (e.g. reaction or evaporation), the resting value can be captured from the last batch or startup
If the process changes are negligible during the setpoint response, the resting value can be estimated as:
the PID output just before the setpoint change for an integrating (e.g. batch) process
the PID output just before the setpoint change plus the setpoint change divided by the process gain for a self-regulating (e.g. continuous) process
For self-regulating processes such as flow with the loop deadtime (  o ) approaching or less than the largest process time constant (  p ), the logic is revised to step the PID output immediately to the resting value. The PID output is held at the resting value for the T 98 process response time (T 98  o  p ).

Integrating Response Time (seconds)  o K i = { [ CV 2  t 2 ]  CV 1  t 1 ] }  CO  CO ramp rate is  CV 1  t 1 ramp rate is  CV 2  t 2 CO CV Integrating process gain (%/sec/%) Response to change in controller output with controller in manual % Controlled Variable (CV) or % Controller Output (CO) observed process deadtime

Loop Block Diagram  p1  p2  p2 K pv  p1  c1  m2  m2  m1  m1 K cv  c  c2 Valve Process Controller Measurement K mv  v  v K L  L  L Load Upset  CV  CO  MV  PV PID Delay Lag Delay Delay Delay Delay Delay Delay Lag Lag Lag Lag Lag Lag Lag Gain Gain Gain Gain Local Set Point  DV Total Observed Dead Time :  o  v  p1  p2  m1  m2  c  v  p1  m1  m2  c1  c2 % % % Delay => Dead Time Lag =>Time Constant K i = K mv  (K pv /  p2 )  K cv 100% / span K c T i T d

 CV  change in controlled variable (%)
 CO  change in controller output (%)
K c  controller gain (dimensionless)
K i  integrating gain (%/sec/% or 1/sec)
K p  process gain (dimensionless) also known as open loop gain
MV  manipulated variable (engineering units)
PV  process variable (engineering units)
 t  change in time (sec)
 o  total loop dead time (sec)
 m  measurement time constant (sec)
 p  process time constant (sec) also known as open loop time constant
T i  integral (reset) time setting (sec/repeat)
T d  derivative (rate) time setting (sec)
T o  oscillation period (sec)
  Lambda (closed loop time constant or arrest time) (sec)
 f   Lambda factor (ratio of closed to open loop time constant or arrest time)
Nomenclature

PID Structure ER is external reset (e.g. secondary PV) Dynamic Reset Limit   SP   proportional derivative  Gain     Rate    CO filter filter CV filter Filter Time   Rate Time  filter Filter Time = Reset Time ER Positive Feedback

PID Structure Choices
PID action on error (  = 1 and  = 1)
PI action on error, D action on PV (  = 1 and  = 0)
I action on error, PD action on PV (  = 0 and  = 0)
PD action on error (  = 1 and  = 1) (no I action)
P action on error, D action on PV (  = 1 and  = 0) (no I action)
ID action on error (  = 1) (no P action)
I action on error, D action on PV (  = 0) (no P action)
Two degrees of freedom controller (  and  adjustable 0 to 1)
The  and  factors do not affect the load response of a control loop

Contribution of Each PID Mode (Step Change in the Set Point)  CO 2 =  CO 1  SP seconds/repeat  CO 1 Time (seconds) Signal (%) 0 kick from proportional mode bump from filtered derivative mode repeat from integral mode For fastest setpoint response we want to maximize kick from proportional mode bump from derivative mode, and setpoint weighting factors (  = 1 and  = 1)

Lambda Tuning for Integrating Processes Integrating Process Gain: Controller Gain: Controller Integral (Reset) Time: Lambda (closed loop arrest time) is defined in terms of a Lambda factor (  f ): Closed loop arrest time for load disturbance Controller Derivative (Rate) Time: To prevent slow rolling oscillations: secondary lag

Primary and secondary K p  secondary  p  primary  p  total  o  K i  K p   p  Tuning for Today’s Example

Demo of Effect of PID Structure 3 on Setpoint Response
Objective – Show slow setpoint response from just integral action
Activities:
For Single Integrating Loop:
Enter tuning settings, Gain = 1.7, Reset = 210 sec, Rate = 2 sec
Set Primary Process Delay = 9 sec, Lag 2 Inc & Lag 2 Dec = 100 sec
Set Primary Process Type = Integrating
Choose controller structure 3 ( I action on error, PD action on PV (  = 0 and  = 0))
Make setpoint change from 50% to 60%

Other Control Loop Opportunities to Reduce Fed-Batch and Startup Time
Output Lead-Lag
A lead-lag on the controller output or in the digital positioner can kick the signal though the valve deadband and sticktion, get past split range points, and make faster transitions from heating to cooling and vice versa.
A lead-lag can potentially provide a faster setpoint response with less overshoot when analyzers are used for closed loop control of integrating processes When combined with the enhanced PID algorithm (PIDPlus) described in:
Deminar #1 http://www.screencast.com/users/JimCahill/folders/Public/media/5acf2135-38c9-422e-9eb9-33ee844825d3
White paper http://www.modelingandcontrol.com/DeltaV-v11-PID-Enhancements-for-Wireless.pdf
Deadtime Compensation
The simple addition of a delay block with the deadtime set equal to the total loop deadtime to the external reset signal for the positive feedback implementation of integral action described in Deminar #3 for the dynamic reset limit option http://www.screencast.com/users/JimCahill/folders/Public/media/f093eca1-958f-4d9c-96b7-9229e4a6b5ba .
The controller reset time can be significantly reduced and the controller gain increased if the delay block deadtime is equal or slightly less than the process deadtime as studied in Advanced Application Note 3 http://www.modelingandcontrol.com/repository/AdvancedApplicationNote003.pdf

Other Control Loop Opportunities to Reduce Fed-Batch and Startup Time
Feed Maximization
Model Predictive Control described in Application Note 1 http://www.modelingandcontrol.com/repository/AdvancedApplicationNote001.pdf
Override control (next slide) is used to maximize feeds to limits of operating constraints via valve position control (e.g. maximum vent, overhead condenser, or jacket valve position with sufficient sensitivity per installed characteristic).
Alternatively, the limiting valve can be set wide open and the feeds throttled for temperature or pressure control. For pressure control of gaseous reactants, this strategy can be quite effective.
For temperature control of liquid reactants, the user needs to confirm that inverse response from the addition of cold reactants to an exothermic reactor and the lag from the concentration response does not cause temperature control problems.
All of these methods require tuning and may not be particularly adept at dealing with fast disturbances unless some feedforward is added. Fortunately the prevalent disturbance that is a feed concentration change is often slow enough due to raw material storage volume to be corrected by temperature feedback.
Profile Control
If you have a have batch measurement that should increase to a maximum at the batch end point (e.g. maximum reaction temperature or product concentration), the slope of the batch profile of this measurement can be maximized to reduce batch cycle time. For application examples checkout “ Direct Temperature Rate of Change Control Improves Reactor Yield ” in a Funny Thing Happened on the Way to the Control Room http://www.modelingandcontrol.com/FunnyThing/ and the Control magazine article “ Unlocking the Secret Profiles of Batch Reactors ” http://www.controlglobal.com/articles/2008/230.html .

Example of Advanced Regulatory Control (reduced batch cycle time by 25%) 08/11/10 feed A feed B coolant makeup CAS ratio CAS reactor vent product maximum production rate 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-2 CAS CAS CAS ZC-3 ZC-4 < Override Control override control ZC-1, ZC-3, and ZC-4 work to keep their respective control valves at a max throttle position with good sensitivity and room for loop to maneuver. ZC-2 will raise TC-1 SP if FC-1 feed rate is maxed out

Sequence Opportunities to Reduce Pure-Batch and Startup Time
Reduce wait times, operator attention requests, and manual actions by automation.
Reduce excess hold times (e.g. heat release can confirm reaction start/end).
Improve charge times and accuracy by better sensor design (e.g. mass flow meters and valve location (e.g. minimize dribble time and holdup).
Minimize acquire time by improved prioritization of users (e.g. unit operation with biggest effect on production rate gets access to feeds and utilities).
Reduce failure expression activation by better instruments, redundancy and signal selection, and more realistic expectations of instrument performance.
Improve failure expression recovery by configuration and displays.
Eliminate steps by simultaneous actions (e.g. heat-up and pressurization).
Increase feed and heat transfer rate by an increase in pump impeller size.
Minimize non constrained processing time by all out run, cutoff, and coast.
Minimize processing time by better end point detection (inferential measurements by neural networks and online or at-line analyzers).
Mid batch correction based on adapted online virtual plant model or batch analytics projection to latent structures (PLS) and first principle relationships.

Demo of Effect of PID Structure 1 on Setpoint Response
Objective – Show faster setpoint response from addition of kick from proportional mode and bump derivative mode
Activities:
Look at setpoint response for structure 3
Choose controller structure 1 (P ID action on error (  = 1 and  = 1))

Identified Responses for Batch Profile Control

Model Predictive Control (MPC) of Growth Rate and Product Formation Rate Product Formation Rate Biomass Growth rate Substrate Dissolved Oxygen

Demo of Effect of SP Feedforward on Setpoint Response
Objective – Show how reduce batch cycle time by use of setpoint feedforward
Activities:
Look at setpoint response for structure 1
Set SP FF Gain = 2

Model Predictive Control (MPC) Reduces Batch Cycle Time Batch Basic Fed-Batch APC Fed-Batch Batch Inoculation Inoculation Dissolved Oxygen (AT6-2) pH (AT6-1) Estimated Substrate Concentration (AT6-4) Estimated Biomass Concentration (AT6-5) Estimated Product Concentration (AT6-6) Estimated Net Production Rate (AY6-12) Estimated Biomass Growth Rate (AY6-11) MPC in Auto

Model Predictive Control (MPC) Improves Batch Predictions Current Product Yield (AY6-10D) Current Batch Time (AY6-10A) Predicted Batch Cycle Time (AY6-10B) Predicted Cycle Time Improvement (AY6-10C) Predicted Final Product Yield (AY6-10E) Predicted Yield Improvement (AY6-10F) Batch Basic Fed-Batch APC Fed-Batch Batch Inoculation Inoculation MPC in Auto Predicted Final Product Yield (AY6-10E) Predicted Batch Cycle Time (AY6-10B)

Demo of Effect of Bang-Bang Control on Setpoint Response
Objective – Show how to reduce batch and startup time by a full throttle setpoint response (bang-bang control)
Activities:
Look at setpoint response for setpoint feedforward
Set SP FF Gain = 0
Set Bang-Bang Bias = 4%

Structure 3 Rise Time = 8.5 min Settling Time = 8.5 min Overshoot = 0% Structure 1 Rise Time = 1.6 min Settling Time = 7.5 min Overshoot = 1.7% Structure 1 + SP FF Rise Time = 1.2 min Settling Time = 6.5 min Overshoot = 1.3% Structure 1 + Bang-Bang Rise Time = 0.5 min Settling Time = 0.5 min Overshoot = 0.2% Summary of Demo Results

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Join Us Aug 25, Wednesday 10:00 am CDT
PID Control of Runaway Processes (How to Improve the Performance of Exothermic Reactor Temperature Loops)
Look for a recording of Today’s Deminar later this week at:
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PID Control of True Integrating Processes - Greg McMillan Deminar

by Jim Cahill

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PID Control of True Integrating Processes - Greg McMillan Deminar Presentation Transcript