PID Tuning for Near Integrating Processes - Greg McMillan Deminar

Interactive Opportunity Assessment Demo and Seminar (Deminar) Series for Web Labs – PID Tuning for Near-Integrating Processes June 23, 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/

Top Ten Keys to Excellent 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 Life and Loop Performance ( 1) Minimizing deadtime

Time (seconds) % Controlled Variable (CV) or % Controller Output (CO)  CO  CV  o  p K p =  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 Response Most temperature loops have a process time constant so much greater than the deadtime, the response is a ramp in the allowable control error about setpoint and are thus termed “ near- integrators”

Lambda Tuning for Self-Regulating Processes Self-Regulation Process Gain: Controller Gain Controller Integral Time Lambda (Closed Loop Time Constant)

Near Integrator Gain Approximation 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 Maximum Disturbance Rejection For max load rejection set lambda equal to deadtime Substitute Into Tuning for max disturbance rejection (Ziegler Nichols reaction curve method gain factor would be 1.0 instead of 0.5) For setpoint response to minimize overshoot

Reduction in Tuning Test Time 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 Then the near integrating tuning test time is reduced by 90%: For our example today: The near integrator tuning time is reduced by 97%!

Demo 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%

Rapid Process Model Identification and Deployment Opportunity For the manipulation of jacket temperature to control vessel temperature, the near integrator gain is 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) 4 CV SUB First Principle Parameters = f ( K i ) CO n-1 Value of controller output (%) from last scan ∆ CO θ o K P K i ∆ CV Switch ODE ( K i ) ∆ CV ∆ CV Sum CV n-1 Value of controlled variable (%) from last scan K P = CV o / CO o process gain approximation  P = K P /K i negative feedback time constant  P + = K P /K i positive feedback time constant Methodology extends beyond loops to any process variable that can be measured and any variable that can be changed CO  P K P  P + 1 2 3 ∆ CV

Rapid Process Model Identification and Deployment Opportunity The observed deadtime ( θ o ) and integrator gain (K i ) 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  θ o ). 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 model Model 3: Slow self-regulating experimental model Model 4: Slow non-self-regulating positive feedback (runaway) experimental model Patent disclosure filed on 3-1-2010

Demo of Near Integrator Tuning Objective – Show how to reduce tuning time for near integrating processes Activities: 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 Change

Values at End of 1 st Deadtime Interval

Values at End of 2 nd Deadtime Interval

Tuning for Today’s Example For setpoint response to minimize overshoot Lambda tuning equations for integrating processes would give similar results if Lambda (arrest time) is set equal to the observed deadtime (see next Deminar for more details)

Demo of Near Integrator Tuning Objective – Show how to reduce tuning time for near integrating processes Activities: 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 10:00 am CDT 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 this week at: www.ModelingAndControl.com www.EmersonProcessXperts.com

PID Tuning for Near Integrating Processes - Greg McMillan Deminar

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PID Tuning for Near Integrating Processes - Greg McMillan Deminar