Process Control Improvement Primer - Greg McMillan Deminar

Interactive Opportunity Assessment Demo and Seminar (Deminar) Series for Web Labs – Process Control Improvement Primer Sept 8, 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 Things You Don’t Want to Hear During Startup” Courtesy of Hunter Vegas (October 2010 Control Talk)
(10) We never really could figure out what the old system was doing.
(9) Do I have a system backup?!? I thought YOU were making backups!
(8) They want to make our startup into a reality show.
(7) The displays are fine and dandy but where are the panel boards?
(6) We have changed our mind – we want the old system back.
(5) Can you reprogram it so the wrong valve still works?
(4) Didn’t you get the revised batch sheets?
(3) Is a blue screen bad??
(2) What is that burning smell?
And the Number 1 thing you don’t want to hear :

“ Top Ten Things You Don’t Want to Hear During Startup” Courtesy of Hunter Vegas (October 2010 Control Talk)
(1) We are out of coffee!

Introduction
There is no clear picture of what is the potential source and size of a process control improvement
Practical process control knowledge is detailed, fragmented, and experience driven
This seminar will attempt to provide a unified approach and understanding of the impact of the PID, final control element (e.g. valve or variable speed drive), process, disturbance, and measurement on loop performance

Unifying Concepts
“ It is all about management of change”
90% of process control improvements involve the following concepts:
Delay
Speed
Gain
Sensitivity-Resolution
Backlash-Deadband
Nonlinearity
Noise
Oscillations
Resonance
Attenuation
Optimum
Delay, speed, and gain are the most prevalent limiting concepts

Delay
“ Without deadtime I would be out of a job”
Fundamentals
A more descriptive name would be total loop deadtime . The loop deadtime is the amount of time for the start of a change to completely circle the control loop and end up at the point of origin. For example, an unmeasured disturbance cannot be corrected until the change is seen and the correction arrives in the process at the same point as the disturbance.
While process deadtime offers a continuous train of values whereas digital devices and analyzers offer non continuous data values at discrete intervals, these delays add a phase shift and increase the ultimate period (decrease natural frequency) like process deadtime.
Goals
Minimize delay (the loop cannot do anything until it sees and enacts change)
Sources
Pure delay from deadtimes and discontinuous updates
Piping, duct, plug flow reactor, conveyor, extruder, spin-line, and sheet transportation delays
Digital devices - scan, update, reporting, and execution times (0.5  T)
Analyzers - sample processing and analysis cycle time (1.5  T)
Sensitivity-resolution limits
Backlash-deadband
Equivalent delay from lags
Mixing
Column trays
Heat transfer surfaces
Thermowells
Electrodes
Transmitter damping
Signal filters

Speed (Rate of Change)
“ Speed kills - (high speed processes and disturbances and low speed control systems can kill performance)”
Fundamentals
The rate of change in 4 deadtime intervals is most important. By the end of 4 deadtimes, the control loop should have completed most of its correction. Thus, the short cut tuning method (Deminar #6) is consistent with performance objectives.
Goals
Make control systems faster and make processes and disturbances slower
Sources
Control system
PID tuning settings (gain, reset, and rate)
Slewing rate of control valves and velocity limits of variable speed drives
Disturbances
Steps - Batch operations, on-off control, manual actions, SIS, startups, and shutdowns
Oscillations - limit cycles, interactions, and excessively fast PID tuning
Ramps - reset action in PID
Process
Mixing in volumes due to agitation, boiling, mass transfer, diffusion, and migration

Gain
“ All is lost if nothing is gained”
Fundamentals
Gain is the change in output for a change in input to any part of the control system. Thus there is a gain for the PID, valve, disturbance, process, and measurement. Knowing the disturbance gain (e.g. change in manipulated flow per change in disturbance) is important for sizing valves and feedforward control.
Goals
Maximize control system gains (maximize control system reaction to change) and minimize process and disturbance gains (minimize process reaction to change).
Sources
PID controller gain
Inferential measurements (e.g. temperature change for composition change in distillation column)
Slope of control valve or variable speed drive installed characteristic (inherent characteristic & system loss curve)
Measurement calibration (100% / span). Important where accuracy is % of span
Process design
Attenuation by volumes (can be estimated)
Attenuation by PID (transfer of variability from controlled to manipulated variables)

Sensitivity-Resolution
“ You cannot control what you cannot see”
Fundamentals
Minimum change measured or manipulated - once past sensitivity limit full change is seen or used but resolution limit will quantize the change (stair step where the step size is the resolution limit). Both will cause a limit cycle if there is an integrator in the process or control system.
Goals
Improve sensitivity and resolution
Sources
In measurements, minimum change detected and communicated (e.g. sensor threshold and wireless update trigger level) and quantized change (A/D & D/A)
Minimum change that can be manipulated (e.g. valve stick-slip sensitivity and speed resolution)

Backlash-Deadband
“ No problem if you don’t ever change direction”
Fundamentals
Minimum change measured or manipulated once the direction is changed - once past backlash-deadband limit full change is seen or used. Both will cause a limit cycle if there are 2 or more integrators in the process or control system.
Goals
Minimize backlash and deadband
Sources
Pneumatic instrument flappers, links, and levers (hopefully these are long gone)
Rotary valve and damper links, connections, and shaft windup
Variable speed drive setup parameter to eliminate hunting and chasing noise

Nonlinearity
“ Not a problem if the process is constant, but then again if the process is constant, you do not need a control system”
Fundamentals
While normally associated with a process gain that is not constant, in a broader concept, a nonlinear system occurs if a gain, time constant, or delay changes anywhere in the loop. All process control systems are nonlinear to some degree.
Goals
Minimize nonlinearity
Sources
Control valve and variable speed drive installed characteristics (flat at high flows)
Process transportation delays (inversely proportional to flow)
Digital and analyzer delays (loop delay depends upon when change arrives in discontinuous data value update interval)
Inferred measurement (conductivity or temperature vs. composition plot is a curve)
Logarithmic relationship (glass pH electrode and zirconium oxide oxygen probe)
Process time constants (proportional to volume and density)

Noise
“ The best thing you can do is not react to noise”
Fundamentals
Extraneous fluctuations in measured or manipulated variables
Goals
Minimize size and frequency of noise and do not transfer noise to process
Sources
Bubbles
Concentration and temperature non-uniformity from imperfect mixing
Electromagnetic interference (EMI)
Ground loops
Interferences (e.g. sodium ion on pH electrode)
Velocity profile non-uniformity
Velocity impact on pressure sensors

Oscillations
“ Oscillations are best kept in control theory textbooks”
Fundamentals
Sine wave, square wave, and saw-tooth periodic disturbances perpetually upset a system and can get amplified by resonance.
Goals
Minimize source and attenuate by controller tuning and process design
Sources
Limit cycles from sensitivity-resolution and backlash-deadband
On-off control (common for sump level control by switches)
Aggressive tuning (common for reactor temperature control)
Excessive reset action (common for level and other integrating processes)

Resonance
“ Don’t make things worse than they already are”
Fundamentals
Oscillation period close to ultimate period can be amplified by feedback control.
Goals
Make oscillation period slower or control loop faster
Sources
Control loops in series with similar loop deadtimes (e.g. multiple stage pH control)
Control loops in series with similar tuning and valve sticktion and backlash
Day to night ambient changes to slow loops (e.g. column temperature control)

Attenuation
“ If you had a blend tank big enough you would not need control”
Fundamentals
Attenuation increases as the volume of the blend tank increases and the ultimate period of the control loop decreases.
Goals
Maximize attenuation by increasing volume and mixing and making loops faster
Sources
Mixed volume size and degree of mixing
Control loop speed

Optimum
“ Most setpoints are not at their optimum”
Fundamentals
The primary loop setpoint is offset from the optimum temperature, pressure, or concentration.
Goals
Minimize offset from optimum
Sources (of non-optimum operation)
Process variability
Measurement error
Sensitivity-resolution
Backlash-deadband
Lack of process knowledge
Process nonlinearity (e.g. catalyst degradation and production rate changes)
Operator preference (e.g. sweet spots)
Incorrect SIS settings

Time (seconds) % Controlled Variable (CV) or % Controller Output (CO)  CO  CV  o  p2 K p =  CV  CO  CV CO CV Self-regulating process open loop negative feedback time constant Self-regulating process gain (%/%) Response to change in controller output with controller in manual observed total loop deadtime Self-Regulating Process Open Loop Response  o or Maximum speed in 4 deadtimes is critical speed

Integrating Process Open Loop Response Maximum speed in 4 deadtimes is critical speed 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 total loop deadtime

Runaway Process Open Loop Response Response to change in controller output with controller in manual  o Noise Band Acceleration  CV  CO  CV K p =  CV  CO Runaway process gain (%/%) % Controlled Variable (CV) or % Controller Output (CO) Time (seconds) observed total loop deadtime runaway process open loop positive feedback time constant For safety reasons, tests are terminated after 4 deadtimes or Maximum speed in 4 deadtimes is critical speed  ’ p2  ’ o

Loop Block Diagram (First Order Approximation)  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 First Order Approximation :  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 process 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)
 t s  sample time (sec)
 o  total loop dead time (sec)
 f  filter time constant (sec)
 m  measurement time constant (sec)
 p2  primary (large) self-regulating process time constant (sec)
 ’ p2  primary (large) runaway process time constant (sec)
 p1  secondary (small) process time constant (sec)
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

Impact of Fast and Slow Disturbances
Objective – Show the effect of disturbance speed
Activities:
For Single Self-Regulating Loop:
Review fast upset test (primary upset lag and reset time = 6 seconds)
Increase primary upset lag to 60 seconds
After about 5 minutes review slow load upset test results

Practical Limit to Loop Performance Peak error decreases as the controller gain increases but is essentially the open loop error for systems when total deadtime >> process time constant Integrated error decreases as the controller gain increases and reset time decreases but is essentially the open loop error multiplied by the reset time plus signal delays and lags for systems when total deadtime >> process time constant Peak and integrated errors cannot be better than ultimate limit - The errors predicted by these equations for the PIDPlus and deadtime compensators cannot be better than the ultimate limit set by the loop deadtime and process time constant

Ultimate Limit to Loop Performance Peak error is proportional to the ratio of loop deadtime to 63% response time Integrated error is proportional to the ratio of loop deadtime squared to 63% response time For a sensor lag (e.g. electrode or thermowell lag) or signal filter that is much larger than the process time constant, the unfiltered actual process variable error can be found from the equation for attenuation

Disturbance Speed and Attenuation Effect of load disturbance lag (  L ) can be estimated by replacing the open loop error with the exponential response of the disturbance during the loop deadtime The attenuation of oscillations van be estimated from the expression of the Bode plot equation for the attenuation of oscillations slower than the break frequency where (  f ) is the filter time constant, electrode or thermowell lag, or a mixed volume residence time

Implied Deadtime from Slow Tuning Slow tuning (large Lambda) creates an implied deadtime where the loop performs about the same as a loop with fast tuning and an actual deadtime equal to the implied deadtime (  i )

Effect of Implied Deadtime on Allowable Digital or Analyzer Delay In this self-regulating process the original process delay (dead time) was 10 sec. Lambda was 20 sec and the sample time was set at 0, 5, 10, 20, 30, and 80 sec (Loops 1 - 6) The loop integrated error increased slightly by 1%*sec for a sample time of 10 sec which corresponded to a total deadtime (original process deadtime + 1/2 sample time) equal to the implied deadtime of 15 seconds. http://www.modelingandcontrol.com/repository/AdvancedApplicationNote005.pdf sample time = 0 sec sample time = 5 sec sample time = 10 sec sample time = 20 sec sample time = 30 sec sample time = 80 sec Effect depends on tuning, which leads to miss-guided generalities based on process dynamics

Fastest Practical PID Tuning Settings (Practical Limit to Loop Performance) For runaway processes: For self-regulating processes: For integrating processes: short cut tuning method (near integrator approximation) short cut tuning method (near integrator approximation)

Effect of Tuning Speed on Oscillatory Disturbance 1 Ultimate Period 1 1 Faster Tuning Log of Ratio of closed loop amplitude to open loop amplitude Log of ratio of disturbance period to ultimate period no attenuation of disturbances resonance (amplification) of disturbances amplitude ratio is proportional to ratio of break frequency lag to disturbance period 1 no better than manual worse than manual improving control

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Join Us Oct 13, Wednesday 10:00 am CDT
PID Deadtime Compensation (How to setup and tune a PID for deadtime compensation)
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Process Control Improvement Primer - Greg McMillan Deminar

by Jim Cahill on Sep 08, 2010

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Process Control Improvement Primer - Greg McMillan Deminar — Presentation Transcript