pid tuning for near integrating processes - greg mcmillan deminar

Interactive Opportunity Interactive Opportunity AssessmentAssessmentInteractive Opportunity Interactive Opportunity AssessmentAssessment

Demo and Seminar (Deminar) Series for Web Labs –

PID Tuning for Near-Integrating PID Tuning for Near-Integrating Processes Processes

June 23, 2010Sponsored by Emerson, Experitec, and Mynah

Created byGreg McMillan and Jack Ahlers

www.processcontrollab.com Website - Charlie Schliesser (csdesignco.com)

[File Name or Event]Emerson Confidential27-Jun-01, Slide 2 Slide 2

WelcomeWelcome WelcomeWelcome 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/


Top Ten Keys to Excellent Top Ten Keys to Excellent Life and Loop Performance Life and Loop Performance Top Ten Keys to Excellent Top Ten Keys to Excellent

Life and Loop Performance Life and Loop Performance (10) Maximized disturbance rejection (9) Adaptation to changes (8) Ignoring noise (7) Exhibiting self-regulation (6) Reaching targets faster (5) Coordinating actions (4) Minimizing oscillations (3) Effectively using feedback (2) Optimizing goals

And the Number 1 Key:


Top Ten Keys to Excellent Top Ten Keys to Excellent Life and Loop PerformanceLife and Loop PerformanceTop Ten Keys to Excellent Top Ten Keys to Excellent Life and Loop PerformanceLife and Loop Performance

(1) Minimizing deadtime


Time (seconds)

% Controlled Variable (CV) or

% Controller Output (CO)

CO

CV

op

Kp = CV CO

CV

CO

CV

self-regulating process time constant

Self-regulating process gain (%/%)

Response to change in controller output with controller in manual

observed process

deadtime

Self-Regulating Process ResponseSelf-Regulating Process ResponseSelf-Regulating Process ResponseSelf-Regulating Process Response

Most temperature loops have a process time constant so

much greater than the deadtime,the response is a ramp in theallowable control error aboutsetpoint and are thus termed

“near- integrators”


Lambda Tuning for Lambda Tuning for Self-Regulating ProcessesSelf-Regulating Processes

Lambda Tuning for Lambda Tuning for Self-Regulating ProcessesSelf-Regulating Processes

CO

CVK p

)( opfp

ic K

TK

piT

Self-Regulation Process Gain:

Controller Gain

Controller Integral Time

pf Lambda (Closed Loop Time Constant)


Near Integrator Gain ApproximationNear Integrator Gain ApproximationNear Integrator Gain ApproximationNear Integrator Gain Approximation

COtCVMaxK

Kp

pi /)/(

For “Near Integrating” gain approximation use maximum ramp rate divided by change in controller output

The above equation can be solved for the process time constant by taking the process gain to be 1.0 or for more sophistication as the

average ratio of the controlled variable to controller output

Tuning test can be done for a setpoint change if the PID gain is > 2 and the PID structure is

“PI on Error D on PV” so you see a step change in controller output from the proportional mode


Fastest Possible Tuning for MaximumFastest Possible Tuning for MaximumDisturbance RejectionDisturbance Rejection

Fastest Possible Tuning for MaximumFastest Possible Tuning for MaximumDisturbance RejectionDisturbance Rejection

oic KK

15.0

opf For max load rejection set lambda equal to deadtime

piT o

p

pi

KK

Substitute

)( op

ic K

TK

Into

Tuning for max disturbance rejection(Ziegler Nichols reaction curve method gain factor would be 1.0 instead of 0.5)

oiT 4

oiT 10For setpoint response to minimize overshoot


Reduction in Tuning Test TimeReduction in Tuning Test TimeReduction in Tuning Test TimeReduction in Tuning Test Time

SRpo

oNI TT

4

3

The near integrating tuning test time (3 deadtimes) as a fraction of the self-regulating tuning test (time to steady state) is:

If the process time constant is greater than 6 times the deadtime

o 6p

Then the near integrating tuning test time is reduced by 90%:

SRNI TT 1.0

For our example today:

sec100p sec4o

The near integrator tuning time is reduced by 97%!

SRNI TT 03.0


Demo of Near Integrator TuningDemo of Near Integrator TuningDemo of Near Integrator TuningDemo of Near Integrator Tuning Objective – Show how to reduce tuning time for near

integrating processes Activities:

– For Single Slow Self-Regulating Loop:• Increase primary process time constant to 100 sec

• With setpoint at 10% and controller in manual, increase output by 40%


4

CVSUB

First Principle Parameters = f (Ki)

COn-1

Value of controller output (%) from

last scan

∆COθo

KP

Ki

∆CV Switch

ODE (Ki)

∆CV

∆CV

Sum

CVn-1

Value of controlled variable (%) from

last scan

KP = CVo / COo process gain approximation

P = KP/Ki negative feedback time constant

P+ = KP/Ki positive feedback time constant

Methodology extends beyond loops to anyprocess variable that can be measured

and any variablethat can be changed

CO

P

KP P+

1

2

3

∆CV

Rapid Process Model Identification Rapid Process Model Identification and Deployment Opportunityand Deployment Opportunity

Rapid Process Model Identification Rapid Process Model Identification and Deployment Opportunityand Deployment Opportunity

For the manipulation of jacket temperature to control vessel temperature, the near integrator gain is

)(/)( opi MCAUK

Since we generally know vessel volume (liquid mass), heat transfer area, and process heat capacity,

We can solve for overall heat transfer coefficient (least known parameter)


Rapid Process Model IdentificationRapid Process Model Identification and Deployment Opportunity and Deployment Opportunity

Rapid Process Model IdentificationRapid Process Model Identification and Deployment Opportunity and Deployment Opportunity

The observed deadtime (θo ) and integrator gain (Ki) are identified after a change in any controller output (e.g. final control element or setpoint) or any disturbance measurement. The identification of the integrator gain uses the fastest ramp rate over a short time period (e.g. 2 dead times) at the start of the process response.

The models are not restricted to loops but can be used to identify the relationship between any variable that can be changed and any affected process variable that can be measured.

The models are used for processes that are have a true integrating response or slow processes with a “near integrating” response (P θo ). The process deadtime and integrating process gain can be used for controller tuning and for plant wide simulations including but not limited to the following types of models:

Model 1: Hybrid ordinary differential equation (ODE) and experimental model Model 2: Integrating process experimental modelModel 3: Slow self-regulating experimental modelModel 4: Slow non-self-regulating positive feedback (runaway) experimental model

Patent disclosure filed on 3-1-2010




– For Single Slow Self-Regulating Loop:• Estimate deadtime and max ramp rate in next two deadtime intervals

• Divide ramp rate by change in controller output to get near integrating process gain


Values at Start of Output ChangeValues at Start of Output ChangeValues at Start of Output ChangeValues at Start of Output Change


Values at End of DeadtimeValues at End of DeadtimeValues at End of DeadtimeValues at End of Deadtime


Values at End of 1Values at End of 1stst Deadtime Interval Deadtime IntervalValues at End of 1Values at End of 1stst Deadtime Interval Deadtime Interval


Values at End of 2Values at End of 2ndnd Deadtime Interval Deadtime IntervalValues at End of 2Values at End of 2ndnd Deadtime Interval Deadtime Interval


Tuning for Today’s ExampleTuning for Today’s ExampleTuning for Today’s ExampleTuning for Today’s Example

sec100p sec4o

5.12401.0

1*5.0

1*5.0

oic KK

4010 oiT

1p K

164 oiT

For setpoint response to minimize overshoot

Lambda tuning equations for integrating processes would give similarresults if Lambda (arrest time) is set equal to the observed deadtime

(see next Deminar for more details)




– For Single Slow Self-Regulating Loop:• Substitute integrating process gain into equation for controller gain

• Set reset time equal to 10 times the deadtime for setpoint response

• Match setpoint to process variable (50%) and put controller in auto

• Make 10% set point change with setpoint response tuning

• Set reset time equal to 4 times the deadtime for load response

• Make 10% set point change with load response tuning

• Make 10% load disturbance


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Join Us July 14, Wednesday Join Us July 14, Wednesday 10:00 am 10:00 am CDTCDTJoin Us July 14, Wednesday Join Us July 14, Wednesday 10:00 am 10:00 am CDTCDT

PID Tuning for True Integrating Processes PID Tuning for True Integrating Processes (How to Reduce Batch Cycle Time for Temperature and Pressure Loops by 25%)

Look for a recording of Today’s Deminar later Look for a recording of Today’s Deminar later this week at:this week at:

www.ModelingAndControl.com

www.EmersonProcessXperts.com


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pid tuning for near integrating processes - greg mcmillan deminar

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