PID Control Of Sampled Measurements - Greg McMillan Deminar Series

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This presentation, PID Control of Sampled Measurements, is from the first in Greg McMillan's live seminar / demo (a.k.a. deminar) series.

You can watch a recorded version of this presentation at: http://www.screencast.com/t/ODhlOWY4M

For future events and background, visit: http://www.emersonprocessxperts.com/archives/2010/04/free_series_of.html

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  • In the PIDPLUS algorithm, the controller responds quickly to change in the set point through proportional action and to change in the feedforward because the controller execution is kept fast. For PI on error and D on PV, the controller only computes integral and derivative contributions when there is a change in the PV reported. The integral contribution approximates a self-regulating process response if Lambda tuning is used where the reset time is set equal to the process time constant. Both the integral and derivative modes use the elapsed time since the last update in their computation instead of the scan time. This provides a longer term view and smoothing action and in particular eliminates spikes from the derivative mode. Both the traditional and wireless PIDPLUS algorithm use a positive feedback network for integral action, which has advantages for cascade and override control and deadtime compensation. Note that the “Dynamic Reset Limit” in FRSPID options must be enabled to use external reset feedback.
  • In this test of a generic integrating process, the sample delay was deliberately chosen to put the loop on the verge of instability. Lambda tuning was used. These tests show that the excessive oscillations caused by a 50% decrease in the reset time in the Lambda tuning equation die out sooner with PIDPLUS Wireless PID is PIDPLUS
  • In this test of a generic integrating process, the sample delay was deliberately chosen to put the loop on the verge of instability. Lambda tuning was used. These tests show that the excessive oscillations caused by a 25% decrease in the Lambda factor in the Lambda tuning equation die out quickly with PIDPLUS. The 25% decrease in Lambda factor corresponds to about a 25% increase in controller gain Wireless PID is PIDPLUS
  • In this test of a generic self-regulating process, the sample delay was deliberately chosen to put the loop on the verge of instability. Lambda tuning was used. These tests show that the excessive oscillations caused by a 50% decrease in the reset time in the Lambda tuning equation don’t exist with PIDPLUS. The response looks as good as the response for the 1.0 reset factor Wireless PID is PIDPLUS
  • In this test of a generic self-regulating process, the sample delay was deliberately chosen to put the loop on the verge of instability. Lambda tuning was used. These tests show that the excessive oscillations caused by a 25% decrease in the Lambda factor in the Lambda tuning equation don’t exist with PIDPLUS. The response looks better than the response for the 2.0 Lambda factor. Further tests indicated the controller gain could be increased to the twice the inverse of the process gain without becoming unstable. In fact the tightest control was achieved when the controller gain was the inverse of the process gain. The 25% decrease in Lambda factor corresponds to about a 25% increase in controller gain Wireless PID is PIDPLUS
  • John Payne from BP used the term Demographic Time Bomb to describe the upcoming crisis in the process industry in finding experienced operations personnel. The US Department of Labor has publicly stated that the average age of energy industry workers is over 50 and half the current work force (more than 500,000 workers) are expected to retire over the next 5 to 10 years. As these workers leave industry they will also take an irreplaceable amount of knowledge and experience. Even the best knowledge transfer program will only be able to recover a fraction of the intelligence that will walk out the gate. While older operators came from a pre-digital age and operated based upon mechanical skills and experience, younger employees, in general, have less mechanical and more computer skills. The old days of operators that would know if a compressor was performing well due to the sound it makes are coming to an end. We have already seen petrochemical and energy plants that are compromised due their lack of qualified operators. One plant in particular stated that if they lost one more of their experienced operators they might not be able to run the plant. While the global economic crisis eroded peoples retirement accounts for the process industries it temporarily slowed down the pace of retirement as operations workers had to hold off on retirement plans until their portfolios recovered. As the equity markets recover, retirements plans will start again. Because, equity markets recover first many process plants will not see their own financial positions strengthen before they have to deal with this crisis. In summary, operations staffing issues are bad now and they will only get worse.
  • The dynamic, off-line simulator is built to provide a virtual control system and plant equivalent to the on-line control system and process in operation and response. In the real plant we have a unit operation, like this distillation column. In order to operate the column safely and profitably we use a control system like DeltaV with transmitters and final control elements. In the virtual control system we use DeltaV Simulate to emulate the operator stations, engineering station, process controllers and higher level system functions. DeltaV Simulate provides an environment where the control system can run in an identical manner as in the actual plant. The transmitters, final control elements, and the process itself are simulated with MiMiC. MiMiC provide complete IO simulation and an environment where the development of complex, dynamic process models is quick and easy.
  • The next releases of MiMiC Simulation Software will allow users to build accurate dynamic simulations for operator training and control system modernization easier and quicker than any other simulation product available. MYNAH’s Advanced Modeling enhancements include the following: Modeling Objects for complex unit operations like distillation columns, separators, power house equipment that provide complete solutions for implementation of complex simulation solutions for common process applications. Modeling functions like the Flash/Thermodynamic functions that encapsulate complex chemical engineering equations in IEC1131 function blocs. Unit Operations Objects that provide easy-to-use objects for solving complex process plant unit operations. A Pressure / Flow network solver that balances the dynamics of continuous, integrated process automatically. Unlike other simulation offerings, MiMiC’s Advanced Modeling capabilities are built for Software Testing and Operator Training, scalable, and cost-effective.
  • The DeltaV Simulate experience allows operator training, control system testing, on a exact replica of the real plant operator station and control system. Unlike emulated systems that may not closely resemble the actual control system, DeltaV Simulate duplicates the control system environment off-line, allowing the operator to fully understand and gain familiarity with the control system without impacting the safe and normal operation.
  • PID Control Of Sampled Measurements - Greg McMillan Deminar Series

    1. 1. Interactive Opportunity Assessment Demo and Seminar (Deminar) Series for Web Labs – PID Enhancement for Sampled Measurements April 7, 2010 Sponsored by Emerson, Experitec, and Mynah Created by Greg McMillan and Jack Ahlers
    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, and was honored by InTech Magazine in 2003 as one of the most influential innovators in automation. 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. The Latest on Smart and Wireless Instrumentation Royalties are donated to the University of Texas Research Campus for Energy and Environmental Resources for Development of Wireless Instrumentation and Control
    4. 4. Top Ten Ways to Make Process Control Enticing <ul><li>(10) Travel programs focusing on the process control systems of cruise ships </li></ul><ul><li>(9) Sci-fi flicks devoted to the process control systems in star ships </li></ul><ul><li>(8) Reality shows where teams compete to improve process control performance </li></ul><ul><li>(7) Entourage shows where groupies use process control to keep their star from self-destruction </li></ul><ul><li>(6) Sport analysis programs where commentators and listeners talk about the dynamics and feedforward control opportunities in football </li></ul><ul><li>(5) Robot movies where advanced parallel processing robots optimize plants </li></ul><ul><li>(4) Detective shows where a special investigator with cute compulsive obsessive habits and an incredibly keen mind for details solves mysterious process control problems </li></ul><ul><li>(3) “Who done it” novels where the culprit is a bad acting control valve </li></ul><ul><li>(2) Web video with cute animal antics in the foreground and engineers talking about process control opportunities in the background </li></ul><ul><li>(1) “Cash for clunkers” programs to replace inefficient old distributed control systems, transmitters, and valves </li></ul>
    5. 5. Self-Learning Web Lab (Web Access Starts May, 2010)
    6. 6. Loop Lab01 Demo of Base Case <ul><li>Show access to “Operate Run” user interface </li></ul><ul><li>Show access to PID faceplate and detail </li></ul><ul><li>Show access to “Process History View” trend chart </li></ul><ul><li>Make setpoint changes to show existing response </li></ul><ul><li>Show access to valve, process, and measurement parameters for sensitivity, noise, and dynamics </li></ul>
    7. 7. Question <ul><li>What will dramatically improve the performance of loops using at-line analyzers making tuning easier and virtually eliminating oscillations? </li></ul><ul><li>What can increase control valve packing life by factor of 10? </li></ul><ul><li>What can make battery life of wireless measurements a non issue? </li></ul><ul><li>What will eliminate the limit cycle from measurement sensitivity limits? </li></ul><ul><li>What will smoothly handle a loss in I/O communication? </li></ul><ul><li>Answer : An Enhanced PID </li></ul>
    8. 8. Loop Lab01 Demo of Sample Time Effect <ul><li>Look at response of traditional PID for base case </li></ul><ul><li>Increase measurement sample time to 20 sec and set sensitivity out of range (e.g. 100%) </li></ul><ul><li>Make setpoint changes to show traditional PID response to large sample time (e.g. twice process time constant) for self-regulating process </li></ul>
    9. 9. <ul><li>PID integral mode is restructured to provide integral action to match the process response in the elapsed time (reset time set equal to process time constant) </li></ul><ul><li>PID derivative mode is modified to compute a rate of change over the elapsed time from the last new measurement value </li></ul><ul><li>PID reset and rate action are only computed when there is a new value </li></ul><ul><li>If transmitter damping is set to make noise amplitude less than sensitivity limit, valve packing and battery life is dramatically improved </li></ul><ul><li>Enhancement compensates for measurement sample time suppressing oscillations and enabling a smooth recovery from a loss in communications further extending packing -battery life </li></ul>http://www.modelingandcontrol.com/repository/WirelessPrimeTime.pdf Traditional and Enhanced PID (PIDPLUS)
    10. 10. Loop Lab01 Demo of Sample Time Effect <ul><li>Look at response of traditional PID for large sample time </li></ul><ul><li>Cut reset time in half (double reset action) </li></ul><ul><li>Make setpoint changes to show traditional PID response to large sample time (e.g. twice process time constant) for self-regulating process with halved reset time </li></ul>
    11. 11. Self-Learning Web Lab (Web Access Starts May, 2010) Checkout on March 10 – April 2 Entries on Integrated and Peak Errors on the website http://www.modelingandcontrol.com/
    12. 12. Peak and Integrated Error Check List
    13. 13. Loop Lab01 Demo of Sample Time Effect <ul><li>Look at response of traditional PID for halved reset time for large sample time </li></ul><ul><li>Change to enhanced PID (PIDPLUS) </li></ul>
    14. 14. Control Studies of Reset Factor & Wireless PID for Real Time Integrating Process (20 sec analyzer sample time) Improvement in stability is significant for any integrating process with analyzer delay Reset Factor = 0.5 Standard PID Standard PID Standard PID Reset Factor = 1.0 Reset Factor = 2.0 Enhanced PID Enhanced PID Enhanced PID Reset Factor = 0.5 Reset Factor = 1.0 Reset Factor = 2.0
    15. 15. Control Studies of Lambda Factor & Wireless PID for Real Time Integrating Process (20 sec analyzer sample time) Improvement in stability is significant for any integrating process with analyzer delay Gain Factor = 1/1.5 Standard PID Standard PID Standard PID Gain Factor = 1/2.0 Gain Factor = 1/2.5 Enhanced PID Enhanced PID Enhanced PID Gain Factor = 1/1.5 Gain Factor = 1/2.0 Gain Factor = 1/2.5
    16. 16. Loop Lab01 Demo of Sample Time Effect <ul><li>Look at response of enhanced PID to see if it decayed the oscillations started by traditional PID </li></ul><ul><li>Make setpoint changes to show enhanced PID response to large sample time (e.g. twice process time constant) for self-regulating process with halved reset time </li></ul>
    17. 17. Control Studies of Reset Factor & Wireless PID for Real Time Self-Regulating Process (40 sec analyzer sample time) Improvement in stability and control is dramatic for any self-regulating process with analyzer delay Standard PID Standard PID Standard PID Reset Factor = 0.5 Reset Factor = 1.0 Reset Factor = 2.0 Enhanced PID Enhanced PID Enhanced PID Reset Factor = 0.5 Reset Factor = 1.0 Reset Factor = 2.0
    18. 18. Control Studies of Lambda Factor & Wireless PID for Real Time Self-Regulating Process (40 sec analyzer sample time) Improvement in stability and control is dramatic for any self-regulating process with analyzer delay Standard PID Standard PID Standard PID Gain Factor = 1/1.5 Gain Factor = 1/2.0 Gain Factor = 1/2.5 Enhanced PID Enhanced PID Enhanced PID Gain Factor = 1/1.5 Gain Factor = 1/2.0 Gain Factor = 1/2.5
    19. 19. Loop Lab01 Demo of Sensitivity Effect <ul><li>Look at setpoint response of enhanced PID with halved reset time for large sample time </li></ul><ul><li>Set measurement sample time to out of range (e.g.1000 sec) and set sensitivity to large value (e.g. 2%) </li></ul><ul><li>Change to traditional PID and make setpoint change to show traditional PID response to poor sensitivity </li></ul>
    20. 20. Top Ten Reasons I Use a Virtual Plant <ul><li>(10) You can’t freeze, restore, and replay an actual plant batch </li></ul><ul><li>(9) No separate programs to learn, install, interface, and support </li></ul><ul><li>(8) No waiting on lab analysis </li></ul><ul><li>(7) No raw materials </li></ul><ul><li>(6) No environmental waste </li></ul><ul><li>(5) Virtual instead of actual problems </li></ul><ul><li>(4) Batches are done in 14 minutes instead of 14 days </li></ul><ul><li>(3) Plant can be operated on a tropical beach </li></ul><ul><li>(2) Last time I checked our wallet I didn’t have $100,000K </li></ul><ul><li>(1) Actual plant doesn’t fit in my suitcase </li></ul>
    21. 21. Virtual Plant Synergy Dynamic Process Model Online Data Analytics Model Predictive Control Loop Monitoring And Tuning DCS batch and loop configuration, displays, and historian Virtual Plant Laptop or Desktop Personal Computer Or DCS Application Station or Controller Embedded Advanced Control Tools Embedded PAT Tools Process Knowledge
    22. 22. Virtual Plant Continuity Explore Discover Prototype Deploy The consistent platform offered by the virtual plant can insure maximum flow of knowledge gained at each step in the commercialization process from bench top to pilot plant to industrial plant operation Train
    23. 23. Loop Lab01 Demo of Sensitivity Effect <ul><li>Look at response of traditional PID with halved reset time for poor measurement sensitivity </li></ul><ul><li>Change to enhanced PID (PIDPLUS) and make setpoint change to show enhanced PID response for poor sensitivity </li></ul>
    24. 24. Demographic Time Bomb <ul><ul><li>Average age of process industry worker is over 50 </li></ul></ul><ul><ul><li>Half of the current work force will retire (more than 500,000 workers) in 5 to 10 years </li></ul></ul><ul><ul><li>Irreplaceable knowledge loss </li></ul></ul><ul><ul><li>Newer generation of workers with less mechanical inclination and exposure </li></ul></ul><ul><ul><li>Scenario for control engineers and technicians may be even more severe due to suspension in hiring in 1980s </li></ul></ul><ul><ul><li>Process plants in danger of closing due to lack of qualified personnel </li></ul></ul><ul><ul><li>Delayed retirement plans will be accelerated as equity markets recover </li></ul></ul>
    25. 25. Virtual Plant Essentials DeltaV Simulate Product Family MiMiC Simulation Software
    26. 26. <ul><li>Dynamic, Accurate Simulations “High Fidelity” </li></ul><ul><li>Advanced IEC Objects and Functions with Streams </li></ul><ul><li>Thermo / Flash / Stream Property Functions </li></ul><ul><li>Advanced Modeling Core Objects </li></ul><ul><ul><li>Vessel, Valve, Pump, PRV, HX, DHX, Stream T </li></ul></ul><ul><ul><li>PF Solver </li></ul></ul><ul><li>Energy Management Objects </li></ul><ul><ul><li>Boiler with Furnace, Steam Header, Desuperheater, Fuel, Turbine </li></ul></ul><ul><li>Distillation Objects </li></ul><ul><ul><li>Column, Reboiler, Stripper </li></ul></ul><ul><li>Separator Objects </li></ul><ul><ul><li>2-phase, 3-phase Separator, Physical Absorber </li></ul></ul>Process Model Development
    27. 27. Graphics and Simulation Control
    28. 28. Loop Lab01 Demo of Sensitivity Effect <ul><li>Look at response of enhanced PID with halved reset time for poor measurement sensitivity to see if it stopped the limit cycle from the traditional PID </li></ul>
    29. 29. Help Us Improve These Deminars! WouldYouRecommend.Us/105679s21/
    30. 30. Join Us April 21, Wed 1:00 CDT <ul><li>PID Control of Valve Sticktion and Backlash </li></ul><ul><li>Look for a recording of Today’s Deminar later this week at: </li></ul><ul><li>www.ModelingAndControl.com </li></ul><ul><li>www.EmersonProcessXperts.com </li></ul>
    31. 31. QUESTIONS?

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