ISA Saint Louis Short Course Dec 6-8, 2010 Exceptional Process Control Opportunities  - An Interactive Exploration of Proc...
Welcome <ul><li>Gregory K. McMillan  </li></ul><ul><ul><li>Greg is a retired Senior Fellow from Solutia/Monsanto and an IS...
Top Ten Things You Don’t Want to Hear  in a Project Definition Meeting <ul><li>(10) I don’t want any smart instrumentation...
<ul><li>“ Without deadtime I would be out of a job” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>A more descriptiv...
<ul><li>“ Speed kills - (high speed processes and disturbances and low speed control systems can kill performance)” </li><...
<ul><li>“ All is lost if nothing is gained” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>Gain is the change in out...
(4) - Resonance <ul><li>“ Don’t make things worse than they already are” </li></ul><ul><li>Fundamentals </li></ul><ul><ul>...
(4) - Resonance  Top Ten Concepts For all of you frequency response and Bode Plot Fans  1 Ultimate Period 1 1 Faster Tunin...
(5) Attenuation <ul><li>“ If you had a blend tank big enough you would not need control” </li></ul><ul><li>Fundamentals </...
The attenuation of oscillations can be estimated from the expression of the Bode plot  equation for the attenuation of osc...
(6) Sensitivity- Resolution  <ul><li>“ You cannot control what you cannot see” </li></ul><ul><li>Fundamentals </li></ul><u...
(6) Sensitivity- Resolution  Top Ten Concepts Sensitivity o x x o x o o o o o o o o o x x x x x x x x Actual Transmitter  ...
(6) Sensitivity- Resolution  Top Ten Concepts Resolution Digital Updates o o o o o o o o o o x x x x x x x x x x o x Actua...
(7) Hysteresis-Backlash <ul><li>“ No problem if you don’t ever change direction” </li></ul><ul><li>Fundamentals </li></ul>...
(7) Hysteresis-Backlash Top Ten Concepts Hysteresis Hysteresis Digital Updates Process Variable  and Measurements Actual T...
(7) Hysteresis-Backlash Top Ten Concepts Backlash (Deadband) Deadband is 5% - 50% without a positioner ! Deadband Signal (...
(8) Repeatability-Noise <ul><li>“ The best thing you can do is not react to noise” </li></ul><ul><li>Fundamentals </li></u...
(8) Repeatability-Noise Top Ten Concepts Official definition of repeatability obtained from calibration tests Process Vari...
(8) Repeatability-Noise Top Ten Concepts Practical definition of repeatability as seen on trend charts Process Variable  a...
(8) Repeatability-Noise Top Ten Concepts Noise as seen on trend charts Process Variable  and Measurements Digital Updates ...
<ul><li>There is always an offset and drift, it is matter of size and consequence </li></ul><ul><li>Fundamentals </li></ul...
(9) Offset-Drift Top Ten Concepts Offset (Bias) 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 Digital Up...
(9) Offset-Drift Top Ten Concepts Drift (Shifting Bias) Process Variable  and Measurements Months 0 1 2 3 4 5 6 7 8 9 10 0...
(10) Nonlinearity <ul><li>“ Not a problem if the process is constant, but then again if the process is constant, you do no...
(10) Nonlinearity Top Ten Concepts 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 Digital Updates 0 1 2 3...
Accuracy and Precision Top Ten Concepts Good Accuracy and Good Precision 2-Sigma Bias 2-Sigma True and Measured  Values Fr...
Self-Regulating Process Open Loop Response Time (seconds) % Controlled Variable (CV)  or % Controller Output (CO)  CO  C...
Integrating Process Open Loop Response Maximum speed in 4 deadtimes is critical speed Improving Dynamics Time (seconds)  ...
Runaway Process Open Loop Response Response to change in controller output with controller in manual  o Noise Band Accele...
Nomenclature <ul><li> CV    change in controlled variable (%) </li></ul><ul><li> CO    change in controller output (%)...
Phase Shift (  ) and Amplitude Ratio (B/A) A B time phase shift  oscillation period T o If the phase shift is -180 o ...
Basis of First Order Approximation    =  Tan -1 (  )    negative phase shift (as    approaches infinity,   appr...
Loop Block Diagram (First Order Approximation)  p1  p2  p2 K pv  p1  c1  m2  m2  m1  m1 K cv  c  c2 Valve Proce...
Nomenclature <ul><li> CV    change in controlled variable (%) </li></ul><ul><li> CO    change in controller output (%)...
Ultimate Limit to Loop Performance Peak error is proportional to the ratio of loop deadtime to 63% response time (Importan...
Disturbance Speed and Attenuation Effect of load disturbance lag (  L ) on peak error can be estimated by replacing the  ...
Effect of Disturbance Lag on Integrating Process Periodic load disturbance time constant  increased by factor of 10 Adapti...
Accessing On-Demand and Adaptive Tuning  Click on magnifying glass to get detail view of limits and tuning Click on Duncan...
Effect of Dynamics Lab <ul><li>Objective   – Show the effect of deadtime on ultimate period and tuning </li></ul><ul><li>A...
Top Ten Things Missing in University    Courses on Process Control <ul><li>(10) Control valves with stick-slip and deadban...
<ul><li>Proportional (P mode)  - increase in gain increases P mode contribution </li></ul><ul><ul><li>Provides an immediat...
Contribution of Each PID Mode  Improving Tuning - Part 1 Contribution of Each PID Mode for a Step Change in the Set Point ...
Reset Gives Operations What They Want SP PV IVP 52 48 ? TC-100 Reactor Temperature steam  valve opens water valve opens 50...
Open  Loop Time Constant (controller in manual) CO Time (seconds) Signal (%) 0  o Dead Time  (Time Delay)   p Open Loop ...
Closed  Loop Time Constant (controller in auto) CO Time (seconds) Signal (%) 0  o Dead Time  (Time Delay)   c Closed Loo...
Conversion of Signals for PID Algorithm To compute controller tuning settings, the process variable and controller output ...
Practical Limit to Loop Performance Peak error decreases as the controller gain increases but is essentially the  open loo...
Implied Deadtime from Slow Tuning Slow tuning (large Lambda) creates an implied deadtime where the loop performs about the...
Effect of Implied Deadtime on   Allowable Digital or Analyzer Delay In this self-regulating process the original process d...
Lambda Tuning for Self-Regulating Processes Self-Regulation Process Gain: Controller Gain Controller Integral Time Lambda ...
Lambda Tuning for Integrating Processes Integrating Process Gain: Controller Gain: Controller Integral (Reset) Time: Lambd...
Fastest Possible Tuning  (Lambda Tuning Method) For max load rejection set lambda equal to deadtime Substitute  Into Tunin...
Near Integrator Approximation  (Short Cut Tuning Method) For “Near Integrating” gain approximation use maximum  ramp rate ...
Fastest Controller Tuning   ( ultimate oscillation method*) K c    K u  T i  = 1.0 *   u T d  = 0.1    u   F...
Fastest Controller Tuning  (reaction curve method*) For runaway processes: For self-regulating processes:  For integrating...
Ultimate Period and Ultimate Gain Improving Tuning - Part 1 Time (min) Measurement (%) Ultimate Gain is Controller Gain th...
Damped Oscillation  - (Proportional Only Control) Time (min) Measurement (%) Offset 110%  of   o Quarter Amplitude Period...
<ul><li>Put the controller in auto at normal setpoint.  </li></ul><ul><li>Choose largest step change in controller setpoin...
Traditional Open Loop Tuning Method  <ul><li>Choose largest step change in controller output that is safe.  </li></ul><ul>...
Short Cut Ramp Rate Tuning Method  <ul><li>Choose largest step change in controller output and setpoint that is safe. If t...
Manual Tuning Lab <ul><li>Objective   – Gain experience with manual tuning methods to appreciate auto tuning </li></ul><ul...
On-Demand Tuning Algorithm Time (min) Ultimate Period T u 0 Set Point d a Ultimate Gain 4   d K u  =   ...
Adaptive Tuning Algorithm  Improving Tuning - Part 2
Improving Tuning - Part 2 Pensacola Reactor Adaptive Control Beta Test
Improving Tuning - Part 2 Pensacola Reactor Adaptive Control Beta Test
Broadley-James Corporation Bioreactor Setup Improving Tuning - Part 2 <ul><li>Hyclone 100 liter Single Use Bioreactor (SUB...
Bioreactor Adaptive Control Performance Improving Tuning - Part 2
Bioreactor Adaptive Tuning Setup Improving Tuning - Part 2
Bioreactor Adaptive Model Viewing Improving Tuning - Part 2
Bioreactor Adaptive Learning Setup Improving Tuning - Part 2
Output comes off high limit at 36.8  o C 0.30  o C overshoot Bioreactor Adaptive Tuning Gain 40 Reset 500 Improving Tuning...
Output comes off high limit at 35.9  o C 0.12  o C overshoot Bioreactor Adaptive Tuning Gain 40 Reset 5,000 Improving Tuni...
0.13  o C overshoot Output comes off high limit at 36.1  o C Bioreactor Adaptive Tuning Gain 40 Reset 10,000 Improving Tun...
0.20  o C overshoot Output comes off high limit at 36.4  o C Bioreactor Adaptive Tuning Gain 40 Reset 15,000 Improving Tun...
0.11  o C overshoot Output comes off high limit at 36.1  o C Bioreactor Adaptive Tuning Gain 80 Reset 15,000 Improving Tun...
Integrating and Runaway Process Tuning <ul><li>It is difficult to prevent overshoot in processes without self-regulation <...
MIT Anna India University Lab  Setup  Improving Tuning - Part 2 http://www.controlglobal.com/articles/2010/LevelControl100...
Improving Tuning - Part 2 Gravity discharge flow makes the level response self-regulating  (increase in level head increas...
Improving Tuning - Part 2 Conical Tank Linear Level Controller Performance
Improving Tuning - Part 2 Conical Tank Adaptive Level Controller Models
Improving Tuning - Part 2 Conical Tank Adaptive Level Controller Performance
Nonlinear Control Valve Lab Improving Tuning - Part 2 Equal Percentage Flow Characteristic
Nonlinear Control Valve Lab Improving Tuning - Part 2 click on PID tag and then Tune
Nonlinear Control Valve Lab Improving Tuning - Part 2 click on PID tag and then Tune
Nonlinear Control Valve Lab Improving Tuning - Part 2 Process gain is approximately proportional to flow for equal percent...
Nonlinear Control Valve Lab Improving Tuning - Part 2 Identification Out Limit that sets deadzone should be set approximat...
Nonlinear Control Valve Lab Improving Tuning - Part 2
<ul><li>Objective  -  Show adaptive control of fast nonlinear self-regulating processes (fast loop with equal percentage v...
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Isa saint-louis-exceptional-opportunities-short-course-day-1

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Presented by Greg McMillan on December 6, 2010 to the ISA St. Louis section.

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  • If the temperature is below set point, should the steam or water valve be open? Based on looking at a faceplate or digital value of temperature on a graphic display, the operator will expect the steam valve to be open. Reset will work towards this end. However, the proper position of the control valves depends upon the trajectory of the process variable (PV). If the temperature is rapidly increasing with a sharp upward slope, the coolant valve should be open. Gain and rate action will recognize the approach to the set point and position the valves correctly to prevent overshoot. In contrast, reset has no sense of direction, and sacrifices long term results for short term gratification, much like corporate policies that cater to Wall Street. The human tendency to be impatient and not visualize the projected response (especially if it is slow or noisy) results in too much reset and not enough gain and rate action.
  • The offset from proportional only control is inversely proportional to the controller gain. For level and temperature loops, where the controller gain can be set high, very little reset action is need to eliminate offset..
  • Isa saint-louis-exceptional-opportunities-short-course-day-1

    1. 1. ISA Saint Louis Short Course Dec 6-8, 2010 Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements - Day 1
    2. 2. Welcome <ul><li>Gregory K. McMillan </li></ul><ul><ul><li>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/ </li></ul></ul>
    3. 3. Top Ten Things You Don’t Want to Hear in a Project Definition Meeting <ul><li>(10) I don’t want any smart instrumentation talking back to me </li></ul><ul><li>(9) Let’s study each loop to see if the valve really needs a positioner </li></ul><ul><li>(8) Lets slap an actuator on our piping valves and use them for control valves </li></ul><ul><li>(7) We just need to make sure the control valve spec requires the tightest shutoff </li></ul><ul><li>(6) What is the big deal about process control, we just have to set the flow per the PFD </li></ul><ul><li>(5) Cascade control seems awfully complex </li></ul><ul><li>(4) The operators can tune the loops </li></ul><ul><li>(3) Let’s do the project for half the money in half the time </li></ul><ul><li>(2) Let’s go with packaged equipment and let the equipment supplier select and design the automation system </li></ul><ul><li>(1) Let’s go out for bids and have purchasing pick the best deal </li></ul>
    4. 4. <ul><li>“ Without deadtime I would be out of a job” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>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. </li></ul></ul><ul><ul><li>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. </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Minimize delay (the loop cannot do anything until it sees and enacts change) </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>Pure delay from process deadtimes and discontinuous updates </li></ul></ul><ul><ul><ul><li>Piping, duct, plug flow reactor, conveyor, extruder, spin-line, and sheet transportation delays (process deadtimes set by mechanical design - remaining delays set by automation system design) </li></ul></ul></ul><ul><ul><ul><li>Digital device scan, update, reporting, and execution times (0.5  T) </li></ul></ul></ul><ul><ul><ul><li>Analyzer sample processing and analysis cycle time (1.5  T) </li></ul></ul></ul><ul><ul><ul><li>Sensitivity-resolution limits </li></ul></ul></ul><ul><ul><ul><li>Backlash-deadband </li></ul></ul></ul><ul><ul><li>Equivalent delay from lags </li></ul></ul><ul><ul><ul><li>Mixing, column trays, dip tube size and location, heat transfer surfaces, and volumes in series (process lags set by mechanical design - remaining lags set by automation system design) </li></ul></ul></ul><ul><ul><ul><li>Thermowells </li></ul></ul></ul><ul><ul><ul><li>Electrodes </li></ul></ul></ul><ul><ul><ul><li>Transmitter damping </li></ul></ul></ul><ul><ul><ul><li>Signal filters </li></ul></ul></ul>(1) - Delay Top Ten Concepts
    5. 5. <ul><li>“ Speed kills - (high speed processes and disturbances and low speed control systems can kill performance)” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>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 (near-integrator) is consistent with performance objectives. </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Make control systems faster and make processes and disturbances slower </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>Control system </li></ul></ul><ul><ul><ul><li>PID tuning settings (gain, reset, and rate) </li></ul></ul></ul><ul><ul><ul><li>Slewing rate of control valves and velocity limits of variable speed drives </li></ul></ul></ul><ul><ul><li>Disturbances </li></ul></ul><ul><ul><ul><li>Steps - Batch operations, on-off control, manual actions, SIS, startups, and shutdowns </li></ul></ul></ul><ul><ul><ul><li>Oscillations - limit cycles, interactions, and excessively fast PID tuning </li></ul></ul></ul><ul><ul><ul><li>Ramps - reset action in PID </li></ul></ul></ul><ul><ul><li>Process </li></ul></ul><ul><ul><ul><li>Degree of mixing in volumes due to agitation, boiling, mass transfer, diffusion, and migration </li></ul></ul></ul>(2)- Speed Top Ten Concepts
    6. 6. <ul><li>“ All is lost if nothing is gained” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>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. </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Maximize control system gains (maximize control system reaction to change) and minimize process and disturbance gains (minimize process reaction to change). </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>PID controller gain </li></ul></ul><ul><ul><li>Inferential measurements (e.g. temperature change for composition change in distillation column) </li></ul></ul><ul><ul><li>Slope of control valve or variable speed drive installed characteristic (inherent characteristic & system loss curve) </li></ul></ul><ul><ul><li>Measurement calibration (100% / span). Important where accuracy is % of span </li></ul></ul><ul><ul><li>Process design </li></ul></ul><ul><ul><li>Attenuation by upstream volumes (can be estimated) </li></ul></ul><ul><ul><li>Attenuation by upstream PID loops (transfer of PV variability to controller output) </li></ul></ul><ul><li>For a discussion of unifying concepts check out Deminar #9 “Process Control Improvement Primer” Sept 8, 2010 Recording: </li></ul><ul><ul><li>http://modelingandcontrol.com/ </li></ul></ul>(3) - Gain Top Ten Concepts
    7. 7. (4) - Resonance <ul><li>“ Don’t make things worse than they already are” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>Oscillation period close to ultimate period can be amplified by feedback control </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Make oscillation period slower or control loop faster </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>Control loops in series with similar loop deadtimes (e.g. multiple stage pH control) </li></ul></ul><ul><ul><li>Control loops in series with similar tuning and valve sticktion and backlash </li></ul></ul><ul><ul><li>Day to night ambient changes to slow loops (e.g. column temperature control) </li></ul></ul>Top Ten Concepts
    8. 8. (4) - Resonance Top Ten Concepts For all of you frequency response and Bode Plot Fans 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
    9. 9. (5) Attenuation <ul><li>“ If you had a blend tank big enough you would not need control” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>Attenuation increases as the volume of the blend tank increases and the ultimate period of the control loop decreases. </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Maximize attenuation by increasing volume and mixing and making loops faster </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>Mixed volume size and degree of mixing </li></ul></ul><ul><ul><li>Control loop speed </li></ul></ul>Top Ten Concepts
    10. 10. The attenuation of oscillations can 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 Equation is also useful for estimating original process oscillation amplitude from filtered oscillation amplitude to better know actual process variability (measurement lags and filters provide a attenuated view of real world) (5) Attenuation Top Ten Concepts
    11. 11. (6) Sensitivity- Resolution <ul><li>“ You cannot control what you cannot see” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>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. </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Improve sensitivity and resolution </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>In measurements, minimum change detected and communicated (e.g. sensor threshold and wireless update trigger level) and quantized change (A/D & D/A) </li></ul></ul><ul><ul><li>Minimum change that can be manipulated (e.g. valve stick-slip sensitivity and speed resolution) </li></ul></ul>Top Ten Concepts
    12. 12. (6) Sensitivity- Resolution Top Ten Concepts Sensitivity o x x o x o o o o o o o o o x x x x x x x x Actual Transmitter Response True Process Variable Process Variable and Measurements Digital Updates 0 1 2 3 4 5 6 7 8 9 10 0.00% 0.09% 0.08% 0.07% 0.06% 0.05% 0.04% 0.03% 0.02% 0.01% 1.00%
    13. 13. (6) Sensitivity- Resolution Top Ten Concepts Resolution Digital Updates o o o o o o o o o o x x x x x x x x x x o x Actual Transmitter Response True Process Variable 0 1 2 3 4 5 6 7 8 9 10 0.00% 0.09% 0.08% 0.07% 0.06% 0.05% 0.04% 0.03% 0.02% 0.01% 1.00% Process Variable and Measurements
    14. 14. (7) Hysteresis-Backlash <ul><li>“ No problem if you don’t ever change direction” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>Hysteresis is the bow in a response curve between full scale traverses in both directions. Normally much smaller and less disruptive than backlash </li></ul></ul><ul><ul><li>Backlash (deadband) is minimum change measured or manipulated once the direction is changed - once past backlash-deadband limit you get full change </li></ul></ul><ul><ul><li>Both Hysteresis and backlash will cause a limit cycle if there are 2 or more integrators in the process or control system. </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Minimize backlash and deadband </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>Pneumatic instrument flappers, links, and levers (hopefully these are long gone) </li></ul></ul><ul><ul><li>Rotary valve and damper links, connections, and shaft windup </li></ul></ul><ul><ul><li>Variable speed drive setup parameter to eliminate hunting and chasing noise </li></ul></ul>Top Ten Concepts
    15. 15. (7) Hysteresis-Backlash Top Ten Concepts Hysteresis Hysteresis Digital Updates Process Variable and Measurements Actual Transmitter Response True Process Variable 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 10 0 x x x x x x x x x x x x x x x x x x x x
    16. 16. (7) Hysteresis-Backlash Top Ten Concepts Backlash (Deadband) Deadband is 5% - 50% without a positioner ! Deadband Signal (%) 0 Stroke (%) Digital positioner will force valve shut at 0% signal Pneumatic positioner requires a negative % signal to close valve
    17. 17. (8) Repeatability-Noise <ul><li>“ The best thing you can do is not react to noise” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>Noise is extraneous fluctuations in measured or manipulated variables </li></ul></ul><ul><ul><li>Repeatability is difference in readings for same true value in same direction </li></ul></ul><ul><ul><li>Often repeatability is confused with noise </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Minimize size and frequency of noise and do not transfer noise to process </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>Noise </li></ul></ul><ul><ul><ul><li>Bubbles </li></ul></ul></ul><ul><ul><ul><li>Concentration and temperature non-uniformity from imperfect mixing </li></ul></ul></ul><ul><ul><ul><li>Electromagnetic interference (EMI) </li></ul></ul></ul><ul><ul><ul><li>Ground loops </li></ul></ul></ul><ul><ul><ul><li>Interferences (e.g. sodium ion on pH electrode) </li></ul></ul></ul><ul><ul><ul><li>Velocity profile non-uniformity </li></ul></ul></ul><ul><ul><ul><li>Velocity impact on pressure sensors </li></ul></ul></ul><ul><ul><li>Repeatability </li></ul></ul><ul><ul><ul><li>Sensitivity and resolution </li></ul></ul></ul>Top Ten Concepts
    18. 18. (8) Repeatability-Noise Top Ten Concepts Official definition of repeatability obtained from calibration tests Process Variable and Measurements Digital Updates 0 1 2 3 4 5 6 7 8 9 10 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 Repeatability 0 0 0 0 0 0 0 0 0 0 Actual Transmitter Response True Process Variable
    19. 19. (8) Repeatability-Noise Top Ten Concepts Practical definition of repeatability as seen on trend charts Process Variable and Measurements Digital Updates 0 1 2 3 4 5 6 7 8 9 10 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% Repeatability 0 x 0 0 0 0 0 0 0 0 0 0 x x x x x x x x x x Actual Transmitter Response True Process Variable
    20. 20. (8) Repeatability-Noise Top Ten Concepts Noise as seen on trend charts Process Variable and Measurements Digital Updates 0 1 2 3 4 5 6 7 8 9 10 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 0 x x x x x x x x x x x Noise Actual Transmitter Response True Process Variable
    21. 21. <ul><li>There is always an offset and drift, it is matter of size and consequence </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>The deviation of the peak in the distribution of actual values from true value </li></ul></ul><ul><ul><li>Drift shows up as a slowly changing offset </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Minimize offset and nonlinearity by smart transmitters and sensor matching and smart tuned digital positioners with accurate internal closure member feedback </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>Manufacturing tolerance, degradation, de-calibration, and installation effects (process and ambient conditions and installation methods and location) </li></ul></ul>(9) Offset-Drift Top Ten Concepts
    22. 22. (9) Offset-Drift Top Ten Concepts Offset (Bias) 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 Digital Updates 0 1 2 3 4 5 6 7 8 9 10 Process Variable and Measurements Bias Actual Transmitter Response True Process Variable x x x x x x x x x x 0
    23. 23. (9) Offset-Drift Top Ten Concepts Drift (Shifting Bias) Process Variable and Measurements Months 0 1 2 3 4 5 6 7 8 9 10 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 0 Actual Transmitter Response True Process Variable x Drift = 1% per month x x x x x x x x x x
    24. 24. (10) Nonlinearity <ul><li>“ Not a problem if the process is constant, but then again if the process is constant, you do not need a control system” </li></ul><ul><li>Fundamentals </li></ul><ul><ul><li>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. </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>Minimize nonlinearity by process and equipment design (e.g. reagents and heat transfer coefficients), smart transmitters and sensor matching, valve selection, signal characterization, and adaptive control </li></ul></ul><ul><li>Sources </li></ul><ul><ul><li>Control valve and variable speed drive installed characteristics (flat at high flows) </li></ul></ul><ul><ul><li>Process transportation delays (inversely proportional to flow) </li></ul></ul><ul><ul><li>Digital and analyzer delays (loop delay depends upon when change arrives in discontinuous data value update interval) </li></ul></ul><ul><ul><li>Inferred measurement (conductivity or temperature vs. composition plot is a curve) </li></ul></ul><ul><ul><li>Logarithmic relationship (glass pH electrode and zirconium oxide oxygen probe) </li></ul></ul><ul><ul><li>Process time constants (proportional to volume and density) </li></ul></ul>Top Ten Concepts
    25. 25. (10) Nonlinearity Top Ten Concepts 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 Digital Updates 0 1 2 3 4 5 6 7 8 9 10 Process Variable and Measurements Nonlinearity Actual Transmitter Response True Process Variable x x x x x x x x x x x 0
    26. 26. Accuracy and Precision Top Ten Concepts Good Accuracy and Good Precision 2-Sigma Bias 2-Sigma True and Measured Values Frequency of Measurements True Value Measured Values Good Accuracy and Poor Precision 2-Sigma 2-Sigma Bias True and Measured Values True Value Measured Values Frequency of Measurements Poor Accuracy and Good Precision 2-Sigma Bias 2-Sigma True and Measured Values True Value Measured Values Frequency of Measurements Poor Accuracy and Poor Precision 2-Sigma 2-Sigma Bias True and Measured Values True Value Measured Values Frequency of Measurements
    27. 27. Self-Regulating Process Open Loop Response 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  o or Maximum speed in 4 deadtimes is critical speed Improving Dynamics
    28. 28. Integrating Process Open Loop Response Maximum speed in 4 deadtimes is critical speed Improving Dynamics 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
    29. 29. 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 Improving Dynamics  ’ p2  ’ o
    30. 30. Nomenclature <ul><li> CV  change in controlled variable (%) </li></ul><ul><li> CO  change in controller output (%) </li></ul><ul><li>K c  controller gain (dimensionless) </li></ul><ul><li>K i  integrating process gain (%/sec/% or 1/sec) </li></ul><ul><li>K p  process gain (dimensionless) also known as open loop gain </li></ul><ul><li>DV = disturbance variable (engineering units) </li></ul><ul><li>MV  manipulated variable (engineering units) </li></ul><ul><li>PV  process variable (engineering units) </li></ul><ul><li> t  change in time (sec) </li></ul><ul><li> t x  execution or update time (sec) </li></ul><ul><li> o  total loop dead time (sec) </li></ul><ul><li> f  filter time constant or well mixed volume residence time (sec) </li></ul><ul><li> m  measurement time constant (sec) </li></ul><ul><li> p2  primary (large) self-regulating process time constant (sec) </li></ul><ul><li> ’ p2  primary (large) runaway process time constant (sec) </li></ul><ul><li> p1  secondary (small) process time constant (sec) </li></ul><ul><li>T i  integral (reset) time setting (sec/repeat) </li></ul><ul><li>T d  derivative (rate) time setting (sec) </li></ul><ul><li>t o  oscillation period (sec) </li></ul><ul><li>  Lambda (closed loop time constant or arrest time) (sec) </li></ul><ul><li> f  Lambda factor (ratio of closed to open loop time constant or arrest time) </li></ul>Improving Dynamics
    31. 31. Phase Shift (  ) and Amplitude Ratio (B/A) A B time phase shift  oscillation period T o If the phase shift is -180 o between the process input A and output B , then the total shift for a control loop is -360 o and the output is in phase with the input (resonance) since there is a -180 o from negative feedback (control error = set point – process variable). This point sets the ultimate gain and period that is important for controller tuning. Improving Dynamics For frequency response and Bode plot fans
    32. 32. Basis of First Order Approximation  =  Tan -1 (  ) negative phase shift (as  approaches infinity,  approaches -90 o phase shift)  t = (-360  T o time shift B 1 AR = ---- = ----------------------- amplitude ratio A [1 + (   ] 1/2 Amplitude ratios are multiplicative (AR = AR 1  AR 2 ) and phase shifts are additive (     )  asis of first order approx method where gains are multiplicative and dead times are additive Improving Dynamics For a self-regulating process
    33. 33. 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 (set by automation system design for flow, pressure, level, speed, surge, and static mixer pH control) % % % Delay <=> Dead Time Lag <=>Time Constant For integrating processes: K i = K mv  (K pv /  p2 )  K cv 100% / span Hopefully  p2 is the largest lag in the loop Improving Dynamics K c T i T d
    34. 34. Nomenclature <ul><li> CV  change in controlled variable (%) </li></ul><ul><li> CO  change in controller output (%) </li></ul><ul><li>K c  controller gain (dimensionless) </li></ul><ul><li>K i  integrating process gain (%/sec/% or 1/sec) </li></ul><ul><li>K p  process gain (dimensionless) also known as open loop gain </li></ul><ul><li>DV = disturbance variable (engineering units) </li></ul><ul><li>MV  manipulated variable (engineering units) </li></ul><ul><li>PV  process variable (engineering units) </li></ul><ul><li> t  change in time (sec) </li></ul><ul><li> t x  execution or update time (sec) </li></ul><ul><li> o  total loop dead time (sec) </li></ul><ul><li> f  filter time constant or well mixed volume residence time (sec) </li></ul><ul><li> m  measurement time constant (sec) </li></ul><ul><li> p2  primary (large) self-regulating process time constant (sec) </li></ul><ul><li> ’ p2  primary (large) runaway process time constant (sec) </li></ul><ul><li> p1  secondary (small) process time constant (sec) </li></ul><ul><li>T i  integral (reset) time setting (sec/repeat) </li></ul><ul><li>T d  derivative (rate) time setting (sec) </li></ul><ul><li>t o  oscillation period (sec) </li></ul><ul><li>  Lambda (closed loop time constant or arrest time) (sec) </li></ul><ul><li> f  Lambda factor (ratio of closed to open loop time constant or arrest time) </li></ul>Improving Dynamics
    35. 35. Ultimate Limit to Loop Performance Peak error is proportional to the ratio of loop deadtime to 63% response time (Important to prevent SIS trips, relief device activation, surge prevention, and RCRA pH violations) Integrated error is proportional to the ratio of loop deadtime squared to 63% response time (Important to minimize quantity of product off-spec and total energy and raw material use) 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 Total loop deadtime that is often set by automation design Largest lag in loop that is ideally set by large process volume Improving Dynamics
    36. 36. Disturbance Speed and Attenuation Effect of load disturbance lag (  L ) on peak error can be estimated by replacing the open loop error with the exponential response of the disturbance during the loop deadtime For E i (integrated error), use closed loop time constant instead of deadtime Improving Dynamics
    37. 37. Effect of Disturbance Lag on Integrating Process Periodic load disturbance time constant increased by factor of 10 Adaptive loop Baseline loop Adaptive loop Baseline loop Primary reason why bioreactor control loop tuning and performance for load upsets is a non issue! Improving Dynamics
    38. 38. Accessing On-Demand and Adaptive Tuning Click on magnifying glass to get detail view of limits and tuning Click on Duncan to get DeltaV Insight for “On-Demand” and “Adaptive” tuning Improving Dynamics
    39. 39. Effect of Dynamics Lab <ul><li>Objective – Show the effect of deadtime on ultimate period and tuning </li></ul><ul><li>Activities: </li></ul><ul><ul><li>Go to Main Display, select Single Loop Lab01, </li></ul></ul><ul><ul><li>Click on AC1-1 PID Faceplate and Click on magnifying glass icon to get Detail display </li></ul></ul><ul><ul><li>Click on Duncan icon for “Tune with Insight” and click on top tab “On Demand Tuning” </li></ul></ul><ul><ul><li>Verify expert option is checked and click on “Test” for “On Demand Tuning” </li></ul></ul><ul><ul><li>Note ultimate period and “ Ziegler-Nichols - PI” tuning settings </li></ul></ul><ul><ul><li>Update PID tuning settings and change mode from Explore to Run </li></ul></ul><ul><ul><li>After run is finished, note metrics, and then click on any block in block diagram of loop </li></ul></ul><ul><ul><li>Click on top tab for Process detail and increase Primary Delay from 1 to 5 sec </li></ul></ul><ul><ul><li>Click on “Test” for “On Demand Tuning” </li></ul></ul><ul><ul><li>Note ultimate period and “ Ziegler-Nichols - PI” tuning settings </li></ul></ul><ul><ul><li>Update PID tuning settings and change mode from Explore to Run </li></ul></ul><ul><ul><li>After run is finished, note metrics, and decrease Primary Delay from 5 to 1 sec </li></ul></ul><ul><ul><li>Click on top tab for Measurements detail and increase Delay from 0 to 4 sec </li></ul></ul><ul><ul><li>Click on “Test” for “On Demand Tuning” </li></ul></ul><ul><ul><li>Note ultimate period and “ Ziegler-Nichols - PI” tuning settings </li></ul></ul><ul><ul><li>Update PID tuning settings and change mode from Explore to Run </li></ul></ul><ul><ul><li>After run is finished, note metrics, and decrease Measurement Delay from 4 to 0 sec </li></ul></ul><ul><ul><li>Restore PID gain to 1.0 and reset time to 10 sec </li></ul></ul>Improving Dynamics
    40. 40. Top Ten Things Missing in University Courses on Process Control <ul><li>(10) Control valves with stick-slip and deadband </li></ul><ul><li>(9) Measurements with repeatability errors and turndown limits </li></ul><ul><li>(8) Volumes with variable mixing and transportation delays </li></ul><ul><li>(7) Process input load disturbance </li></ul><ul><li>(6) Control action (direct & reverse) & valve action (inc-open & inc-close) </li></ul><ul><li>(5) PID algorithms using percent </li></ul><ul><li>(4) PID structure, anti-reset windup, output limits, and dynamic reset </li></ul><ul><li>(3) Industry standards for function blocks and communication </li></ul><ul><li>(2) “Control Talk” </li></ul><ul><li>(1) My books </li></ul>Improving Tuning - Part 1
    41. 41. <ul><li>Proportional (P mode) - increase in gain increases P mode contribution </li></ul><ul><ul><li>Provides an immediate reaction to magnitude of measurement change to minimize peak error and integrated error for a disturbance </li></ul></ul><ul><ul><li>Too much gain action causes fast oscillations (close to ultimate period) and can make noise and interactions worse </li></ul></ul><ul><ul><li>Provides an immediate reaction to magnitude of setpoint change for P action on Error to minimize rise time (time to reach setpoint) </li></ul></ul><ul><ul><li>Too much gain causes falter in approach to setpoint </li></ul></ul><ul><li>Integral (I mode) - increase in reset time decreases I mode contribution </li></ul><ul><ul><li>Provides a ramping reaction to error (SP-PV) to minimize integrated error if stable (since error is hardly ever exactly zero, integral action is always ramping the controller output) </li></ul></ul><ul><ul><li>Too much integral action causes slow oscillations (slower than ultimate period) </li></ul></ul><ul><ul><li>Too much integral action causes an overshoot of setpoint (no sense of direction) </li></ul></ul><ul><li>Derivative (D mode) - increase in rate time increases D mode contribution </li></ul><ul><ul><li>Provides an immediate reaction to rate of change of measurement change to minimize peak error and integrated error for a disturbance </li></ul></ul><ul><ul><li>Too much rate action causes fast oscillations (faster than ultimate period) and can make noise and interactions worse </li></ul></ul><ul><ul><li>Provides an immediate reaction to rate of change of setpoint change for D action on Error to minimize rise time (time to reach setpoint) </li></ul></ul><ul><ul><li>Too much rate causes oscillation in approach to setpoint </li></ul></ul>Contribution of Each PID Mode Improving Tuning - Part 1
    42. 42. Contribution of Each PID Mode Improving Tuning - Part 1 Contribution of Each PID Mode for a Step Change in the Set Point (  and  )  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
    43. 43. Reset Gives Operations What They Want SP PV IVP 52 48 ? TC-100 Reactor Temperature steam valve opens water valve opens 50% set point temperature time PV Should steam or water valve be open ? Improving Tuning - Part 1
    44. 44. Open Loop Time Constant (controller in manual) CO Time (seconds) Signal (%) 0  o Dead Time (Time Delay)  p Open Loop (process) Time Constant (Time Lag) CV SP Controller is in Manual Open Loop Error E o (%) 0.63  E o Improving Tuning - Part 1
    45. 45. Closed Loop Time Constant (controller in auto) CO Time (seconds) Signal (%) 0  o Dead Time (Time Delay)  c Closed Loop Time Constant (Time Lag) Lambda (  ) CV SP Controller is in Automatic  SP (%) 0.63   SP Improving Tuning - Part 1
    46. 46. Conversion of Signals for PID Algorithm To compute controller tuning settings, the process variable and controller output must be converted to % of scale and time units of deadtimes and time constants must be same as time units of reset time and rate time settings! Improving Tuning - Part 1 Sensing Element Control Valve AO PID SCLR AI SCLR SCLR % % % SUB CV SP % CO OUT (e.u.) Process Equipment Smart Transmitter PV - Primary Variable SV - Second Variable* TV - Third Variable* FV - Fourth Variable* PV (e.u.) PID DCS MV (e.u.) The scaler block (SCLR) that convert between engineering units of application and % of scale used in PID algorithm is embedded hidden part of the Proportional-Integral-Derivative block (PID) Final Element Measurement * - additional HART variables PV (e.u.)
    47. 47. 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 Open loop error for fastest and largest load disturbance Improving Tuning - Part 1
    48. 48. 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 ) For most aggressive tuning Lambda is set equal to observed deadtime (implied deadtime is equal to observed deadtime) Money spent on improving measurement and process dynamics (e.g. reducing measurement delays and process deadtimes) will be wasted if the controller is not tuned faster to take advantage of the faster dynamics You can prove most any point you want to make in a comparison of control system performance, by how you tune the PID. Inventors of special algorithms as alternatives to the PID naturally tend to tune the PID to prove their case. For example Ziegler-Nichols tuning is often used to show excessive oscillations that could have be eliminated by cutting gain in half Improving Tuning - Part 1
    49. 49. 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 Improving Tuning - Part 1
    50. 50. Lambda Tuning for Self-Regulating Processes Self-Regulation Process Gain: Controller Gain Controller Integral Time Lambda (Closed Loop Time Constant) Lambda tuning excels at coordinating loops for blending, fixing lower loop dynamics for model predictive control, and reducing loop interaction and resonance Improving Tuning - Part 1
    51. 51. 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 Improving Tuning - Part 1
    52. 52. Fastest Possible Tuning (Lambda Tuning Method) 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 Improving Tuning - Part 1
    53. 53. Near Integrator Approximation (Short Cut Tuning Method) 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 Improving Tuning - Part 1
    54. 54. Fastest Controller Tuning ( ultimate oscillation method*) K c  K u  T i = 1.0 *  u T d = 0.1   u For integrating processes or for self-regulating processes where  p >>  o , double the factor for reset time (0.5 => 1.0) and add rate time if the process noise is negligible. The oscillations associated with quarter amplitude decay is about ½ the ultimate gain. Thus if we use quarter amplitude decaying oscillations for the test, we take ½ of the controller gain that caused these oscillations to get ¼ of the ultimate gain These tuning equations provide maximum disturbance rejection but will cause some overshoot of setpoint response Improving Tuning - Part 1 * - Ziegler Nichols method closed loop modified to be more robust and less oscillatory
    55. 55. Fastest Controller Tuning (reaction curve method*) For runaway processes: For self-regulating processes: For integrating processes: Near integrator (  p2 >>  o ): Near integrator (  ’ p2 >>  o ): Deadtime dominant (  p2 <<  o ): Improving Tuning - Part 1 These tuning equations provide maximum disturbance rejection but will cause some overshoot of setpoint response * - Ziegler Nichols method closed loop modified to be more robust and less oscillatory
    56. 56. Ultimate Period and Ultimate Gain Improving Tuning - Part 1 Time (min) Measurement (%) Ultimate Gain is Controller Gain that Caused these Nearly Equal Amplitude Oscillations (K u ) Set Point Ultimate Period T u 0 If  p  o then T u  If  p  o then  u 
    57. 57. Damped Oscillation - (Proportional Only Control) Time (min) Measurement (%) Offset 110% of  o Quarter Amplitude Period T q 0 Improving Tuning - Part 1 Set Point
    58. 58. <ul><li>Put the controller in auto at normal setpoint. </li></ul><ul><li>Choose largest step change in controller setpoint that is safe. Increase the reset time by a factor of 10x for test. </li></ul><ul><li>Add a PV filter to keep the controller output fluctuations from noise within the valve deadband. </li></ul><ul><li>Step the controller setpoint. If the response is non-oscillatory, increase the controller gain and step the controller setpoint in opposite direction. Repeat until you get a slight oscillation (ideally ¼ amplitude decay). Make sure the controller output is not hitting the controller output limits and is on the sensitive part of the control valve’s installed characteristic. </li></ul><ul><li>Estimate the period of the oscillation. Reduce the controller gain until the oscillation disappears (½ current gain), set the reset time equal to ½ the period, and the rate time equal to ¼ of the reset time. If the oscillation is noisy or resembles a square wave or the controller gain is high (e.g. > 10), set the rate time to zero. The factors are ½ the ultimate period and twice the ultimate gain factors because the controller gain that triggered the ¼ amplitude oscillation is about ½ the ultimate gain and the ¼ amplitude period is larger than the ultimate period. </li></ul><ul><li>If a high controller gain is used (e.g. > 10) use setpoint rate of change (velocity) limits if a big kick in the controller output for setpoint changes from proportional action on error (  > 0 ) is disruptive to operations. </li></ul><ul><li>Make setpoint changes across the range of operation to make sure an operating point with a higher controller gain or larger process deadtime does not cause oscillations. Monitor the loop closely over several days of operation. </li></ul>Damped Oscillation Tuning Method Improving Tuning - Part 1
    59. 59. Traditional Open Loop Tuning Method <ul><li>Choose largest step change in controller output that is safe. </li></ul><ul><li>Add a PV filter to keep the controller output fluctuations from noise within the valve deadband. </li></ul><ul><li>Make a change in controller output in manual. </li></ul><ul><li>Note the time it take for the process variable to get out of the noise band as the loop deadtime. </li></ul><ul><li>Estimate the process time constant as the time to reach 63% of the final value. </li></ul><ul><li>Estimate the process gain as final change in the process variable (%) after it reaches a steady state divided by change in the controller output (%). </li></ul><ul><li>To use reaction curve tuning, set the controller gain equal to ½ the process time constant divided by the product of the process gain and deadtime. </li></ul><ul><li>If the process lag is much larger than the loop deadtime, set the reset time setting equal to 4x the deadtime and set the rate time setting equal to the deadtime. If process lag is much smaller than the loop deadtime, set the reset time to 0.5x the loop deadtime and the rate time to zero. </li></ul><ul><li>If a high controller gain is used (e.g. > 10) use setpoint rate of change (velocity) limits if a big kick in the controller output for setpoint changes is disruptive to operations (for PID structures with  > 0 ). </li></ul><ul><li>Make setpoint changes across the range of operation to make sure an operating point with a higher controller gain or larger process deadtime does not cause oscillations. Monitor the loop closely over several days of operation. </li></ul>Improving Tuning - Part 1
    60. 60. Short Cut Ramp Rate Tuning Method <ul><li>Choose largest step change in controller output and setpoint that is safe. If the test is to be made in auto, increase the reset time by factor of 10x for test. </li></ul><ul><li>Add a PV filter to keep the controller output fluctuations from noise within the valve deadband. Measure the initial rate of change of the process variable (  PV 1 /  t). </li></ul><ul><li>Make a either a change in controller output in manual or change in set point in auto </li></ul><ul><li>Note the time it take for the for the process variable to get out of the noise band as the loop deadtime. </li></ul><ul><li>Estimate the rate of change of the process variable (  PV 2 /  t) over successive deadtime intervals (at least two). Choose the largest rate of change. Subtract this from initial rate of change of the process variable and divide the result by the step change in controller output to get the integrating process gain. </li></ul><ul><li>To use reaction curve tuning, set the controller gain equal to 0.4 the inverse of the product of integrating process gain and loop deadtime (Equation 7). </li></ul><ul><ul><li>If the inverse of the integrating gain is much larger than the loop deadtime, set the reset time setting equal to 4x the process deadtime and set the rate time setting equal to the process deadtime, otherwise set the reset time to 0.5x the process deadtime and the rate time to zero </li></ul></ul><ul><li>If a high controller gain is used (e.g. > 10) use setpoint rate of change (velocity) limits if a big kick in the controller output for setpoint changes is disruptive to operations (for PID structures with  > 0 ) </li></ul><ul><li>Make setpoint changes across the range of operation to make sure an operating point with a higher controller gain or larger process deadtime does not cause oscillations. Monitor the loop closely over several days of operation. </li></ul>Improving Tuning - Part 1
    61. 61. Manual Tuning Lab <ul><li>Objective – Gain experience with manual tuning methods to appreciate auto tuning </li></ul><ul><li>Activities: </li></ul><ul><ul><li>Go to Main Display, and select Single Loop Lab01 </li></ul></ul><ul><ul><li>Click on any block in block diagram </li></ul></ul><ul><ul><li>In Process detail, set Primary Process Lag 2 = 30 sec for Inc and Dec </li></ul></ul><ul><ul><li>Click on AC1-1 PID Faceplate and Click on magnifying glass icon to get Detail display </li></ul></ul><ul><ul><li>Tune PID with damped oscillation method and note tuning settings </li></ul></ul><ul><ul><li>Tune PID with traditional open loop method and note tuning settings </li></ul></ul><ul><ul><li>Tune PID with short cut tuning method and note tuning settings </li></ul></ul>Improving Tuning - Part 1
    62. 62. On-Demand Tuning Algorithm Time (min) Ultimate Period T u 0 Set Point d a Ultimate Gain 4  d K u =   e n e = sq rt (a 2 - n 2 ) If n = 0, then e = a alternative to n is a filter to smooth PV Signal (%) Improving Tuning - Part 2
    63. 63. Adaptive Tuning Algorithm Improving Tuning - Part 2
    64. 64. Improving Tuning - Part 2 Pensacola Reactor Adaptive Control Beta Test
    65. 65. Improving Tuning - Part 2 Pensacola Reactor Adaptive Control Beta Test
    66. 66. Broadley-James Corporation Bioreactor Setup Improving Tuning - Part 2 <ul><li>Hyclone 100 liter Single Use Bioreactor (SUB) </li></ul><ul><li>Rosemount WirelessHART gateway and transmitters for measurement and control of pH and temperature. (pressure monitored) </li></ul><ul><li>BioNet lab optimized control system based on DeltaV </li></ul>
    67. 67. Bioreactor Adaptive Control Performance Improving Tuning - Part 2
    68. 68. Bioreactor Adaptive Tuning Setup Improving Tuning - Part 2
    69. 69. Bioreactor Adaptive Model Viewing Improving Tuning - Part 2
    70. 70. Bioreactor Adaptive Learning Setup Improving Tuning - Part 2
    71. 71. Output comes off high limit at 36.8 o C 0.30 o C overshoot Bioreactor Adaptive Tuning Gain 40 Reset 500 Improving Tuning - Part 2
    72. 72. Output comes off high limit at 35.9 o C 0.12 o C overshoot Bioreactor Adaptive Tuning Gain 40 Reset 5,000 Improving Tuning - Part 2
    73. 73. 0.13 o C overshoot Output comes off high limit at 36.1 o C Bioreactor Adaptive Tuning Gain 40 Reset 10,000 Improving Tuning - Part 2
    74. 74. 0.20 o C overshoot Output comes off high limit at 36.4 o C Bioreactor Adaptive Tuning Gain 40 Reset 15,000 Improving Tuning - Part 2
    75. 75. 0.11 o C overshoot Output comes off high limit at 36.1 o C Bioreactor Adaptive Tuning Gain 80 Reset 15,000 Improving Tuning - Part 2
    76. 76. Integrating and Runaway Process Tuning <ul><li>It is difficult to prevent overshoot in processes without self-regulation </li></ul><ul><li>Controller gain adds self-regulation via closed loop response </li></ul><ul><li>Examples of integrating processes (ramping response) are </li></ul><ul><ul><li>Liquid and solids level </li></ul></ul><ul><ul><li>furnace, column, or vessel pressure </li></ul></ul><ul><ul><li>batch composition, pH, or temperature </li></ul></ul><ul><li>Examples of runaway processes (accelerating response) are </li></ul><ul><ul><li>exothermic reactor temperature </li></ul></ul><ul><ul><li>strong acid - strong base pH </li></ul></ul><ul><ul><li>exponential growth phase biomass </li></ul></ul><ul><ul><li>compressor speed during surge </li></ul></ul><ul><li>An overdrive of the controller output beyond its resting value is needed to reach a set point or compensate for a disturbance (achieved by high controller gain) </li></ul><ul><li>The maximum allowable controller gain for many integrating processes is well beyond the comfort level of most users. Measurement noise and resolution often sets the practical high limit to the controller gain rather than process dynamics </li></ul><ul><li>Too much reset action (too small of a reset time) cause severe overshoot </li></ul><ul><li>A higher controller gain creates more overdrive for small setpoint changes and gets controller off it’s output limit sooner for large setpoint changes </li></ul><ul><li>There is a window of allowable controller gains. </li></ul><ul><ul><li>Instability from too high of a controller gain (not likely for industrial processes) </li></ul></ul><ul><ul><li>Slow rolling oscillations from too low of a controller gain (common case) that slowly decay for integrating processes but can grow for runaway processes till it hits physical limits </li></ul></ul>Improving Tuning - Part 2
    77. 77. MIT Anna India University Lab Setup Improving Tuning - Part 2 http://www.controlglobal.com/articles/2010/LevelControl1002.html
    78. 78. Improving Tuning - Part 2 Gravity discharge flow makes the level response self-regulating (increase in level head increases flow through discharge valve) Increase in cross sectional area with level increases process time constant making process response slower Conical Tank Detail
    79. 79. Improving Tuning - Part 2 Conical Tank Linear Level Controller Performance
    80. 80. Improving Tuning - Part 2 Conical Tank Adaptive Level Controller Models
    81. 81. Improving Tuning - Part 2 Conical Tank Adaptive Level Controller Performance
    82. 82. Nonlinear Control Valve Lab Improving Tuning - Part 2 Equal Percentage Flow Characteristic
    83. 83. Nonlinear Control Valve Lab Improving Tuning - Part 2 click on PID tag and then Tune
    84. 84. Nonlinear Control Valve Lab Improving Tuning - Part 2 click on PID tag and then Tune
    85. 85. Nonlinear Control Valve Lab Improving Tuning - Part 2 Process gain is approximately proportional to flow for equal percentage flow characteristic
    86. 86. Nonlinear Control Valve Lab Improving Tuning - Part 2 Identification Out Limit that sets deadzone should be set approximately equal to valve deadband and stick-slip near closed position
    87. 87. Nonlinear Control Valve Lab Improving Tuning - Part 2
    88. 88. <ul><li>Objective - Show adaptive control of fast nonlinear self-regulating processes (fast loop with equal percentage valve) </li></ul><ul><li>Activities: </li></ul><ul><ul><li>Go to Main Display, select Cascade Loop Lab02 </li></ul></ul><ul><ul><li>Click on any block, in Control Valve detail set Equal % Characteristic in Table </li></ul></ul><ul><ul><li>Click on secondary loop AC1-2 PID Faceplate and put PID in Auto </li></ul></ul><ul><ul><li>Click on magnifying glass icon to get Detail display </li></ul></ul><ul><ul><li>Click on Duncan icon for “Tune with Insight” </li></ul></ul><ul><ul><li>Run “ On-Demand Tuner ” (set Ziegler-Nichols - PI factors: 0.2*Ku and 0.6*Tu) </li></ul></ul><ul><ul><li>In “ Models Viewing” , set number of regions = 5 and state parameter as “OUT” </li></ul></ul><ul><ul><li>Go to settings , and set boundaries for each region </li></ul></ul><ul><ul><ul><li>Region 1 0 => 35% </li></ul></ul></ul><ul><ul><ul><li>Region 2 35 => 60% </li></ul></ul></ul><ul><ul><ul><li>Region 3 60 => 75% </li></ul></ul></ul><ul><ul><ul><li>Region 3 75 => 90% </li></ul></ul></ul><ul><ul><ul><li>Region 3 90 => 100% </li></ul></ul></ul><ul><ul><li>In “ Adaptive Tuner ”, set Lambda time = reset time </li></ul></ul><ul><ul><li>With Adaptive Mode “Off” make 2 setpoint changes in each region </li></ul></ul><ul><ul><li>Review “ Adaptive Control ” screen </li></ul></ul><ul><ul><li>Review “ Model Viewing ” screen </li></ul></ul><ul><ul><li>Review “ Simulate ” screen </li></ul></ul><ul><ul><li>With Adaptive Mode “Partial” make same setpoint changes in each region </li></ul></ul>Nonlinear Control Valve Lab Improving Tuning - Part 2
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